Minimal impact of methane on satellite-era regional climate change

Minimal impact of methane on satellite-era regional climate change | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Physical Sciences - Article Minimal impact of methane on satellite-era regional climate change Masaki Toda, Tiffany Shaw, Sarah Kang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6097952/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The attribution of global and regional climate change to anthropogenic greenhouse gases (GHGs) is well appreciated. Existing estimates based on radiative forcing studies suggest carbon dioxide (CO 2 ) has dominated global warming since 1850 with methane (CH 4 ) the second largest contribution. However, radiative forcing studies involve several assumptions and the attribution of GHGs contributions for other climate change indicators is unknown. Here we quantify the impact of individual GHGs on climate change indicators, including regional climate change, in the satellite era using an attribution approach of counterfactual single-forcing CO 2 , CH 4 , and other GHGs coupled climate model simulations. CO 2 dominates global warming, Arctic Sea ice loss, extreme temperatures and regional warming over North America in the satellite era with CH 4 and other GHGs contributing merely around 20% and 30% of the CO 2 contribution, respectively. The results demonstrate that, on multi-decadal or longer time scales, CO 2 dominates and the contribution of CH 4 and other GHGs is small and not distinguishable from noise, especially for regional climate changes. Thus, CH 4 mitigation may not be as effective as previously thought, particularly for regional scale impacts. Earth and environmental sciences/Climate sciences/Climate change/Attribution Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Figures Figure 1 Figure 2 Figure 3 Figure 4 Main article Well-mixed greenhouse gas (GHG) concentrations have increased significantly in the historical era since 1850 and the increase has been attributed to human activities 1 . Many physical climate changes (rising global-mean temperature, extreme heat, Arctic sea ice loss, etc) have been attributed to rising GHG concentrations in the historical (since 1850) and satellite (since 1979) eras 1 . As global warming continues unabated 2 and impacts accumulate, losses and damages are discussed 3 and mitigation options have been proposed for carbon dioxide (CO 2 ) 4,5,6 and methane (CH 4 ) 7,8,9,10,11 . It is important to attribute the contribution of specific GHGs to climate change indicators, including at the regional scale, in order to make effective policy decisions. Two approaches have emerged to link increased GHG concentrations to climate change indicators. Attribution studies use global climate models to simulate counterfactual worlds with or without the increase of anthropogenic GHG concentrations 12,13,14 . The attribution approach is the basis of quantifying human influence from GHGs on global and regional climate changes 1,15,16 . Radiative forcing studies use global climate models with fixed sea surface temperatures to estimate the radiative forcing due to increased GHG concentrations or emissions 17,18,19,20 , which is then used to estimate global warming 21,22,23,24 . Both approaches agree that GHGs have contributed 1.5 °C (1-2 °C for 90% range) to global warming between 1850-1900 and 2000-2019 (IPCC AR6 WG1 Fig. SPM2b,c) 1 . Previous assessments of the contribution of specific GHGs to historical global warming draw exclusively on radiative forcing studies 21,22,23,24,25 . More specifically, radiative forcing studies suggest CO 2 has contributed about half of the GHG-driven warming (~0.79 °C) since 1850, CH 4 contributes 0.51 °C (0.29-0.84 °C for 90% range) and other GHGs contribute 0.19 °C (IPCC AR6 Fig. SPM2c) 1 . Since 1750 the CH 4 contribution to global warming is roughly 28% of the CO₂ contribution considering the direct effect of prescribed concentration increase, but up to 67% if indirect effects, e.g., chemical feedbacks, are considered (IPCC AR6 Fig. 6.12, 7.7) 21,22 . While emissions-driven estimates allow for more complexity (chemical feedbacks 26 ) there is considerable uncertainty in estimating emissions from observations 27,28 and chemical feedbacks themselves also entail uncertainty 29,30 . Regardless, previous assessments lend support to the idea of methane as an effective short-term mitigation option 7 . Radiative forcing studies have several limitations. They assume fixed sea surface temperature 17,18,19,20 and subsequently either use a fixed climate feedback parameter or a simple emulator (e.g., 2-layer energy balance model) 21,22,23,24,25 . Thus, radiative forcing studies do not account for temporal changes in climate feedback parameters 31,32 and often do not account for chemical feedbacks 33 . In addition, radiative forcing studies only provide information about global warming; they do not provide information about other climate change indicators, including regional climate change. Finally, radiative forcing studies do not account for the impact of natural climate variability, resulting from atmosphere-ocean coupling 34 , which significantly impacts regional climate change 35 . Radiative forcing studies provide one line of evidence quantifying the contribution of individual GHGs to historical global warming. It is important to have multiple lines of evidence and assess the impact of individual GHGs across a broad suite of climate change indicators including regional climate change, which has not been reported before. It is especially important to do this for the satellite era where the rate of warming is higher than earlier in the historical era 36 and there are more observations. Here we follow the attribution approach of counterfactual single-forcing coupled global climate model simulations used extensively to attribute human influence from GHGs (IPCC WG1 Fig. SPM2b) 1,12,15 in order to quantify the impact of individual GHGs on climate change indicators beyond global warming, including for regional climate change. We quantify the impact of individual GHGs using single-forcing simulations in which CO 2 , CH 4 and other GHG concentrations change one at a time over the historical era (1850-2020, see Methods). We also consider a counterfactual world where everything but CH 4 changes. This concentration driven approach is the basis of all attribution statements of human influence from GHGs on global and regional climate change 1,12,15,16 . It is a natural intermediate step before moving to emission-driven simulations that may suffer from large uncertainty. We primarily focus on quantifying the contribution of different GHGs on climate change indicators, such as Arctic Sea ice concentration, maximum surface air temperature (SAT), ocean heat content, and regional warming during the satellite era. We also briefly examine their contribution to global warming during the historical era. We use a large-ensemble to compare the contribution of individual GHGs with the role of natural climate variability, which is known to significantly influence regional climate change 35 . Attribution of global-mean climate change to individual GHGs The observed global warming since 1850 is captured by the coupled global climate model simulations (compare solid and dashed black lines in Fig. 1a). Global warming exceeds the range of temperature change from natural variability and thus the signal is clearly distinguishable from the noise (black bars and whiskers do not overlap zero in Fig. 1b). As expected, CO 2 is the primary driver of historical global warming (compare black and red lines in Fig. 1a). The methane single forcing simulation shows that CH 4 is the second largest contributor among the GHGs, with impacts nearly comparable to the combined effect of all other GHGs (compare green and blue lines in Fig. 1a). Focusing on global-mean warming from 1850-1900 to 2010-2019, CO 2 contributes 0.8 °C, CH 4 contributes 0.2 °C and other GHGs contribute 0.26 °C (Fig. 1b). Methane and other GHGs’ global warming contribution is 24% and 31% of that of carbon dioxide during this time period, respectively. Across different time periods, methane’s global warming contribution ranges between 25% and 40% of that from carbon dioxide (Extended Data Fig. 1a). The above results remain unchanged whether methane's contribution is derived from the methane single forcing experiment or by subtracting the all-but-CH₄ forcing simulation from the all-forcing simulation (compare green bar and the star marker in Fig. 1b, Extended Data Fig. 1b), indicating the small nonlinearity of methane. Previous assessments based on radiative forcing studies have only quantified the contribution of different GHGs to global warming since 1850 or 1750. Reliable observations of other global warming indicators (global-land-mean maximum SAT, Arctic sea ice fraction, and global-mean ocean heat content) have only emerged in the satellite era since 1979, which corresponds to a period of accelerated global warming (Fig. 1a). The all-forcing simulation captures the evolution of these global warming indicators in the satellite era (compare black bars and crosses in Fig. 2). The single forcing simulations indicate that during the satellite era, CO 2 contributes 67% of the global warming (compare black and red bars, Fig. 2a). CH 4 is 16% of the CO 2 contribution (green bar, Fig. 2a), smaller than the contribution from all other GHGs which represent 33% of the CO 2 contribution (blue bar, Fig. 2a). The contribution of individual GHGs to other global warming indicators, including global-land-mean maximum SAT (see Methods), Arctic sea ice fraction, and global-mean ocean heat content are very similar to those for global warming (Fig. 2b-d, Extended Data Fig. 2b-d). Namely, CO 2 is the dominant contributor, accounting for roughly 60-66% of the total change. Methane contributes approximately 21-26% of the CO 2 contribution, and other GHGs contribute approximately 31-34% of the CO 2 contribution. Across all climate change indicators the CO 2 contribution exceeds the noise (red bar and whiskers are not overlapping zero, Fig. 2). However, the contribution of methane and other GHGs are not detectable given the noise (green and blue bars and whiskers are overlapping zero, Fig. 2) with the exception of ocean heat content. This is likely because ocean heat content is governed by the deep ocean, making it less sensitive to natural variability. Overall, during the satellite era, CO 2 dominates the change in all global warming indicators with methane the second largest contribution. Methane’s contribution is smaller than that for the historical era and is negligible compared to natural variability, which may be linked to the stagnation of atmospheric CH 4 concentrations since 1985 (Extended Data Fig. 3). Attribution of satellite-era warming over North America to individual GHGs The counterfactual simulations allow us to attribute the contribution of different GHGs to regional warming, which has emerged in the satellite era. Here we focus on warming over North America following previous work 35 but similar results are found for other continents (Extended Data Fig. 4). Focusing on regional warming is important because it is in these scales where the impacts of climate change are most directly experienced. The satellite-era warming over North America is captured by the all-forcing simulation (compare solid and dashed black lines in Fig. 3a). The distribution of satellite era trends across all ensemble members is well separated from zero (Fig. 3b), consistent with the signal dominating over the noise of natural variability. The spatial distribution of the ensemble mean trend shows warming across the entire North American continent (Fig. 3c). However, the spatial pattern of ensemble members with the highest and lowest warming trends in the ensemble vary significantly, indicating an important role for natural variability (Extended Data Fig. 5). CO 2 dominates the warming over North America in the satellite era with a clear positive trend since 1979 (Fig. 3d). The contribution of CO 2 accounts for 61% of the warming over North America in the satellite era. The distribution of satellite era trends for all CO2 members exhibit a positive trend, demonstrating that CO 2 -induced warming is robust and contributes significantly to warming across the North American continent (Fig. 3f) The contribution of CH 4 and other GHGs to warming over North America is minimal in the satellite era, appearing nearly flat in the time series (green and blue solid lines in Fig. 3g,j). For both CH 4 and other GHGs, 22.5% of the ensemble members involve a cooling trend (green and blue curves in Fig. 3h,k). Thus the impact of methane and other GHGs for continental-scale warming is difficult to detect. Consistently, the distribution of trends in response to CO 2 forcing is statistically different from those of CH 4 (p value < 0.001) and other GHGs (p value < 0.001) (see Methods). Methane and other GHGs also do not significantly contribute to the spatial pattern of warming over North America (Fig. 3i,l). The limited contribution of methane is further confirmed by examining the counterfactual world where all but CH 4 changes. Despite the complete absence of methane, North America robustly warms (Fig. 3m). The distribution of satellite era trends across all ensemble members with all forcings and all but CH 4 largely overlap (black and purple curves, Fig. 3n), indicating a broad similarity. Furthermore, the spatial patterns of warming at the continental scale for all forcings and all but CH4 are nearly indistinguishable (Fig. 3o). For other climate change indicators such as Arctic sea ice and maximum SAT over North America, CO 2 also dominates and the contributions of methane and other GHGs are not significant compared to natural variability (Extended Data Figs. 6 and 7). In particular, as many as 37.5% of the CH4 members show increased Arctic sea ice fraction in the satellite era, consistent with it being highly influenced by natural variability 37 . Overall, CO 2 plays a dominant role in the warming over North America and other continents (Extended Data Fig. 4) as well as other climate change indicators during the satellite era. Conversely, the contributions of methane and other GHGs are negligible and, importantly, indistinguishable from natural climate variability. Implications for GHG mitigation The attribution results indicate that CO 2 has been the dominant driver of all climate change indicators from 1850 to 2020. The dominant contribution of CO 2 can be also highlighted by comparing global warming and the cumulative CO₂ emissions. Global warming is proportional to cumulative CO₂ emissions during the historical era for both all forcing and the CO 2 single forcing simulation 38,39 (Fig. 4a). This linear relationship holds even for warming over North America (Fig. 4b). These results lend further support to the idea that carbon dioxide removal (CDR) is an effective way to mitigate global and regional warming. Methane has also been proposed as a means to mitigate global warming, especially on short time scales. However, our attribution results based on single forcing simulations indicate that methane makes a small contribution to historical and satellite era climate change (Fig. 1, 2a-c and 3g). Consistently, historical global and regional warming do not scale linearly with methane emissions (black line, Fig. 4c,d). Although the CH 4 contribution to global and regional warming from the single forcing CH 4 simulation does scale linearly with cumulative methane emissions, its warming contribution is minimal (green line, Fig. 4c,d). These results suggest that methane mitigation has a limited effect on warming over multi-decadal or longer timescales. Methane has been proposed as an attractive mitigation option because of its short atmospheric lifetime of approximately 10 years 40 , which is significantly shorter than CO 2 . However, the decadal warming rates in the all forcing and all but CH 4 simulations are nearly unaffected by the absence of CH 4 , suggesting that methane’s contribution is limited even on decadal timescales (Fig. 4d,e). This implies that the effects of methane mitigation may be difficult to detect even on a decadal timescale due to the influence of natural variability and natural forcing (Extended Data Fig. 8). Finally, CO 2 and methane have both been proposed as mitigation options in the context of the 1.5 °C target set out by the Paris Agreement. In the all-forcing simulation, the ensemble mean reaches 1.5 °C in July 2033 (Methods, Extended Data Fig. 9). While this timing is delayed by 8 years in the counterfactual world excluding CH 4 , in terms of the ensemble mean (Methods, Extended Data Fig. 9), the time of emergence across ensembles exhibits a considerable overlap between the two simulations. Given that the all but CH 4 simulation represents an extreme mitigation scenario, which is unlikely to be achieved in reality, the results suggest the contribution of realistic methane mitigation efforts would be minimal in delaying the 1.5 °C threshold. Summary and Discussion To date the only line of evidence quantifying the contributions of individual GHGs to climate change indicators comes from radiative forcing studies focused on global warming. Here, we conduct counterfactual global climate model simulations to attribute historical and satellite-era changes in SAT, maximum near-surface temperature, Arctic sea ice fraction, and ocean heat content to individual GHGs at both global and regional scales. During the historical era (1850-2019), CO 2 contributed approximately 0.80 °C of global warming, while CH 4 and other GHGs contributed around 0.20 °C and 0.26 °C, respectively, making methane's impact about 25% of that of CO 2 . However, in the satellite era (since 1979), methane's contribution declined further, representing only 16–24% of CO 2 ’s effect on climate change indicators, and these signals are not detectable given natural variability. For regional warming at the continental scale CO 2 remains the dominant contributor, while the contribution of methane and other GHGs to the climate change signal does not exceed the noise. The dominance of CO 2 for global and regional climate change across all climate change indicators supports CO 2 mitigation efforts like CDR. However, our results suggest that the effectiveness of methane mitigation is very limited not only on multi-decadal time scales but also on decadal time scales. This is because natural variability and natural forcing have a greater influence on decadal warming rates than methane. Furthermore, the results suggest methane reduction would delay crossing the 1.5 °C threshold by only a few years at most. Our attribution study results for global warming in the historical era are in good agreement with the results of radiative forcing studies. Thus multiple lines of evidence support the dominance of carbon dioxide with methane as the second largest contribution across all climate change indicators. The contribution of individual GHGs may be sensitive to the inclusion of feedbacks from biology (plant stomatal resistance) and atmospheric chemistry (tropospheric ozone 41 , and stratospheric water vapor 42 ), which require emissions driven simulations that are more uncertain. Here, as a first step, we focused on a concentration driven approach, which is the basis of attribution studies that have quantified the human influence of GHGs on various climate change indicators 1,15,16 . Declarations Acknowledgements We would like to thank Thomas Radattz for his contributions to conducting many of the numerical experiments and for providing valuable guidance to M.T. in carrying them out. This work was supported by the National Science Foundation (grant AGS-2300037 to T.A.S.) and the National Oceanic Atmospheric Administration (grant NA23OAR4310597 to T.A.S.). Author Contribution statement T.A.S. initiated the study. M.T., S.K., and T.A.S. designed the model experiments.. M.T. conducted all of the analysis and wrote the manuscript with feedback from S.K. and T.A.S.. All authors discussed the results and provided feedback on the manuscript. Competing financial interests The authors declare that they have no competing financial interests. References IPCC Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2021); doi:10.1017/9781009157896.001. NASA. Temperatures Rising: NASA Confirms 2024 Warmest Year on Record. https://www.nasa.gov/news-release/temperatures-rising-nasa-confirms-2024-warmest-year-on-record/ (2024). IPCC Summary for Policymakers In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC 2022); doi:10.1017/9781009325844.001. Achakulwisut, P., Erickson, P., Guivarch, C. et al. Global fossil fuel reduction pathways under different climate mitigation strategies and ambitions. Nat Commun 14 , 5425 (2023). https://doi.org/10.1038/s41467-023-41105-z Oyewole, K. A., Okedere, O. B., Rabiu, K. O., Alawode, K. O., & Oyelami, S. Carbon dioxide emission, mitigation and storage technologies pathways. Sustainable Environment 9, (2023). https://doi.org/10.1080/27658511.2023.2188760 National Academies of Sciences, Engineering, and Medicine. Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. Washington, DC: The National Academies Press (2019). https://doi.org/10.17226/25259 National Academies of Sciences, Engineering, and Medicine. A Research Agenda Toward Atmospheric Methane Removal. Washington, DC: The National Academies Press (2024). https://doi.org/10.17226/27157 Nisbet, E. G., Fisher, R. E., Lowry, D., France, J. L., Allen, G., Bakkaloglu, S., et al. Methane mitigation: methods to reduce emissions, on the path to the Paris agreement. Reviews of Geophysics 58 , (2020). https://doi.org/10.1029/2019RG000675 Arndt, A.N. Hristov, W.J. Price, S.C. McClelland, A.M. Pelaez, S.F. Cueva, J. Oh, J. Dijkstra, A. Bannink, A.R. Bayat Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 C target by 2030 but not 2050 Proc. Natl. Acad. Sci., 119 (2022), Article e2111294119. Global Methane Pledge. *Global Methane Pledge*. https://www.globalmethanepledge.org/ (accessed 25 Jan. 2025). Drew Shindell et al. Simultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food Security. Science 335 , 183-189 (2012) https://doi.org/10.1126/science.1210026. Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., Santer, B. D., Stone, D., and Tebaldi, C.: The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6, Geosci. Model Dev. 9 , 3685–3697 (2016), https://doi.org/10.5194/gmd-9-3685-2016 Gillett, N.P., Kirchmeier-Young, M., Ribes, A. et al. Constraining human contributions to observed warming since the pre-industrial period. Nat. Clim. Chang. 11 , 207–212 (2021). https://doi.org/10.1038/s41558-020-00965-9 Aurélien Ribes et al. ,Making climate projections conditional on historical observations.Sci. Adv. 7 , eabc0671 (2021). https://doi.org/10.1126/sciadv.abc0671 Eyring, V., et al. Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 423–552 (2021). https://doi.org/10.1017/9781009157896.005 Deser, C., et al. Isolating the Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New CESM1 Large Ensemble Community Resource. Journal of Climate 33 , 7835–58 (2020). https://doi.org/10.1175/JCLI-D-20-0123.1 Pincus, R., Forster, P. M., and Stevens, B. The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6, Geosci. Model Dev. 9 , 3447–3460 (2016). https://doi.org/10.5194/gmd-9-3447-2016 Smith, C. J., Kramer, R. J., Myhre, G., Forster, P. M., Soden, B. J., Andrews, T., et al. Understanding rapid adjustments to diverse forcing agents. Geophysical Research Letters, 45 , 12,023–12,031 (2018). https://doi.org/10.1029/2018GL079826 Smith, C.J. et al., Effective radiative forcing and adjustments in CMIP6 models. Atmospheric Chemistry and Physics 20 (16), 9591–9618 (2020). https://doi.org/10.5194/acp-20-9591-2020. Thornhill, G. D. et al. Effective radiative forcing from emissions of reactive gases and aerosols–a multi-model comparison. Atmos. Chem. Phys. 21 , 853–874 (2021). https://doi.org/10.5194/acp-21-853-2021 Forster, P., T. et al. The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 923–1054 (2021) https://doi.org/10.1017/9781009157896.009 Szopa, S. et al. Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 817–922 (2021). https://doi.org/10.1017/9781009157896.008 Leach, N. J., Jenkins, S., Nicholls, Z., Smith, C. J., Lynch, J., Cain, M., Walsh, T., Wu, B., Tsutsui, J., and Allen, M. R. FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration. Geosci. Model Dev. 14 , 3007–3036 (2021). https://doi.org/10.5194/gmd-14-3007-2021 Nicholls, Z. R. J., et al. Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response. Geosci. Model Dev. 13 , 5175–5190, (2020). https://doi.org/10.5194/gmd-13-5175-2020 Cain, M., Lynch, J., Allen, M.R. et al. Improved calculation of warming-equivalent emissions for short-lived climate pollutants. npj Clim Atmos Sci 2 , 29 (2019). https://doi.org/10.1038/s41612-019-0086-4 Collins, W. J. et al. AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6, Geosci. Model Dev. 10 , 585–607 (2017). https://doi.org/10.5194/gmd-10-585-2017 Solazzo, E., Crippa, M., Guizzardi, D., Muntean, M., Choulga, M., and Janssens-Maenhout, G. Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases, Atmos. Chem. Phys. 21 , 5655–5683 (2021). https://doi.org/10.5194/acp-21-5655-2021 Rosentreter, J. A., Alcott, L., Maavara, T., Sun, X., Zhou, Y., Planavsky, N. J., & Raymond, P. A. Revisiting the global methane cycle through expert opinion. Earth's Future 12 , e2023EF004234, (2024). https://doi.org/10.1029/2023EF004234 Thornhill, G. et al. Climate-driven chemistry and aerosol feedbacks in CMIP6 Earth system models, Atmos. Chem. Phys. 21 , 1105–1126 (2021). https://doi.org/10.5194/acp-21-1105-2021 Heimann, I., Griffiths, P. T., Warwick, N. J., Abraham, N. L., Archibald, A. T., & Pyle, J. A. Methane emissions in a chemistry-climate model: Feedbacks and climate response. Journal of Advances in Modeling Earth Systems 12 , e2019MS002019 (2020). https://doi.org/10.1029/2019MS002019 Andrews, T., J. M. Gregory, and M. J. Webb The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models. J. Climate 28 , 1630–1648 (2015). https://doi.org/10.1175/JCLI-D-14-00545.1. Armour, K. C., C. M. Bitz, and G. H. Roe Time-Varying Climate Sensitivity from Regional Feedbacks. J. Climate 26 , 4518–4534 (2013). https://doi.org/10.1175/JCLI-D-12-00544.1. Richardson, T.B., et al. Efficacy of climate forcings in PDRMIP models. J. Geophys. Res. Atmos. 124 , no. 23, 12824-12844 (2019). https://doi.org/10.1029/2019JD030581. Battisti, D. S., D. J. Vimont, and B. P. Kirtman 100 Years of Progress in Understanding the Dynamics of Coupled Atmosphere–Ocean Variability. Meteor. Monogr. 59 , 8.1–8.57 (2019). https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0025.1. Deser, C., Knutti, R., Solomon, S. et al. Communication of the role of natural variability in future North American climate. Nature Clim Change 2 , 775–779 (2012). https://doi.org/10.1038/nclimate1562 NASA Earth Observatory. (n.d.). World of Change: Global Temperatures. NASA. Retrieved February 7, 2025, from https://earthobservatory.nasa.gov/world-of-change/global-temperatures England, M., A. Jahn, and L. Polvani Nonuniform Contribution of Internal Variability to Recent Arctic Sea Ice Loss. J. Climate 32 , 4039–4053 (2019). https://doi.org/10.1175/JCLI-D-18-0864.1. Matthews, H., Gillett, N., Stott, P. et al. The proportionality of global warming to cumulative carbon emissions. Nature 459 , 829–832 (2009). https://doi.org/10.1038/nature08047 Canadell, J.G. Global Carbon and other Biogeochemical Cycles and Feedbacks. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 673–816 (2021). https://doi.org/10.1017/9781009157896.007. Prather, M. J., Holmes, C. D., & Hsu, J. Reactive greenhouse gas scenarios: Systematic exploration of uncertainties and the role of atmospheric chemistry. Geophysical Research Letters, 39 (9), L09803 (2012). https://doi.org/10.1029/2012GL051440 Fiore, A. M., D. J. Jacob, B. D. Field, D. G. Streets, S. D. Fernandes, and C. Jang Linking ozone pollution and climate change: The case for controlling methane, Geophys. Res. Lett., 29 (19), 1919 (2002). https://doi.org/10.1029/2002GL015601 Charlesworth, E., Plöger, F., Birner, T. et al. Stratospheric water vapor affecting atmospheric circulation. Nat Commun 14 , 3925 (2023). https://doi.org/10.1038/s41467-023-39559-2002019 Methods Methods Coupled Climate Model simulations We use MPI-ESM1.2-LR 43 for conducting the series of experiments. This version of the Earth System Model has also been utilized in the Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth System Model consisting of the atmospheric model ECHAM6.3, the ocean model MPIOM1.6, the ocean biogeochemistry model HAMOCC6, and the land surface model JSBACH3.2. The atmosphere has 47 vertical levels with a horizontal resolution of approximately 200 km. The ocean model has 40 vertical levels and a horizontal resolution of approximately 150 km. Here we make use of 50 historical simulations (ALL) driven by the CMIP6 suite of historical forcing, including concentrations of long-lived GHGs (CO 2 , CH 4 , N 2 O, chlorofluorocarbons (CFCs)), anthropogenic aerosols, ozone, land use change and natural forcing (stratospheric aerosol, solar irradiance, orbital changes). We conducted 40 member single forcing simulations where all GHGs evolve (denoted GHG), which is the standard GHG attribution simulation 12 . This is further divided into single-forcing GHG simulations: CO2, CH4, and other GHG (N2O plus CFCs), respectively, are varied while all other GHGs are held constant at 1850 levels. The responses to single-forcing GHG simulations sum approximately to the response to all-GHG simulation (compare filled black circle and orange bar in Figs. 1b and 2, Extended Data Fig. 1c). This near-linearity allows us to attribute the climate response to each GHG forcing. To estimate the methane’s contribution more closely in particular with respect to nonlinear responses in climate, we conducted simulations with all historical forcing but methane (all but CH4). Finally, we also performed anthropogenic aerosols only (AER) and natural forcing only (NAT) experiments with 30 members, where anthropogenic aerosols and natural forcings are varied respectively, while everything else is fixed at pre-industrial levels. See Extended Data Table 1 for an overview of the ensembles and the forcing applied for each of them. All simulations are integrated for the period 1850-2020, following the CMIP6 historical simulation protocol for 1850–2014 and the SSP245 scenario for the last six years. Regarding maximum near surface temperature, only 30 members are available for CO2, CH4, other, GHG, and all but CH4 for a technical reason. Observation datasets For observed surface air temperature, we use the average of three data sets, Berkeley Earth 44 , NOAAGlobalTemp version 6.0.0 45 , and HadCRUT5.0.2.0 46 from 1850-2020. For land mean maximum near surface temperature observations, we use the annual maximum of daily maximum from HadEX3 47 from 1978-2017 (TXx annual). For Arctic sea ice fraction observations, we use Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 2 48 from 1978–2020. For ocean heat content, we use the best estimate of IPCC AR6 WG1 Fig. 3.26 15 . Estimation of the timing of 1.5 °C warming since the pre-industrial era The timing of 1.5 °C warming since the pre-industrial era is determined by linearly extrapolating the monthly global mean SAT anomaly time series 49 . For each ensemble member of ALL and all but CH₄, the 1850–1900 monthly climatology is computed and subtracted from the monthly global mean SAT time series to obtain the monthly SAT anomaly time series. The timing of 1.5 °C is then determined by linearly extrapolating this monthly anomaly time series. The extrapolation is based on the 360-month time series from January 1991 to December 2020. The ensemble mean timing for ALL and all but CH₄ scenarios is obtained by first computing the ensemble mean SAT anomaly time series, and then performing the extrapolation. It is not derived by averaging the timing of individual ensemble members. Definition of maximum near surface temperature In this study, maximum near surface temperature is defined as the annual maximum of the daily maximum near surface temperature. For the model output, the highest temperature at each time step within a day is extracted as daily data, and the highest temperature of the year is then identified. Since HadEX3 contains missing values in some regions, when calculating the regional average of maximum near surface temperature of model simulations, areas with missing values in HadEX3 are masked out on the model grid. Cumulative methane emission Cumulative methane emissions are calculated from 1850 onward using annual natural methane emissions and anthropogenic methane emissions from Kleinen et al. (2021) 50 (see their Figures 2 and 3). Significance test A significance test is performed for the difference between ensemble means of two experiments with Welch’s t-test (two-tailed), where the static value t is defined as Here, X̅ is the average of a sample, n is the sample size, and s is the corrected sample standard deviation. Subscripts 1,2 denote different samples. The test uses a t -distribution with integer degrees of freedom that is closest to If members in a sample can be assumed to be independent, for example in a test of the difference between the ensemble means of different two experiments, assign the sample size to n . Data and code availability statement Berkeley Earth is available at https://berkeleyearth.org/data/. NOAAGlobalTemp version6.0.0 is available at https://www.ncei.noaa.gov/data/noaa-global-surface-temperature/v6/access/. HadCRUT5.0.2.0 is available at https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/download.html. HadEX3 annual TXx is available at https://data.ceda.ac.uk/badc/ukmo-hadobs/data/derived/MOHC/HadOBS/HadEX3/v3-0-4/. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 2 is available at https://nsidc.org/data/nsidc-0051/versions/2. The best estimate of observed ocean heat content by IPCC AR6 is available at https://catalogue.ceda.ac.uk/uuid/85168e39bfff444ba02bf55e7682f73d/. Cumulative CO 2 emission since 1850 is available at https://catalogue.ceda.ac.uk/uuid/cfe938e70f8f4e98b0622296743f7913/. The natural CH 4 emission is available at https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_060_ds00007. The anthropogenic CH 4 emission is available at https://aims2.llnl.gov/search/input4MIPs/. The data archiving of the fully coupled experiments using MPI-ESM is underway. The archiving of python codes used for creating the figures in this study are also underway. Methods-only references 43: Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., et al. Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems 11 , 998–1038 (2019). https://doi.org/10.1029/2018MS001400 44: Rohde, R. A., and Z. Hausfather The Berkeley Earth Land/Ocean Temperature Record. Earth Syst. Sci. Data 12 , 3469–3479, (2020). https://doi.org/10.5194/essd-12-3469-2020 45: Huang, Boyin; Yin, Xungang; Menne, Matthew J.; Vose, Russell S.; and Zhang, Huai-Min. NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 6.0.0. NOAA National Centers for Environmental Information (2024). https://doi.org/10.25921/rzxg-p717 46: Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., et al. An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set. Journal of Geophysical Research: Atmospheres 126 , e2019JD032361 (2021). https://doi.org/10.1029/2019JD032361. 47: Dunn, R. J. H., et al. Observed global changes in sector-relevant climate extremes indices - an extension to HadEX3, ESS 11 , e2023EA003279 (2024). https://doi.org/10.1029/2023EA003279 48: DiGirolamo, N., Parkinson, C. L., Cavalieri, D. J., Gloersen, P. & Zwally, H. J. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. (NSIDC-0051, Version 2). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center (2022). https://doi.org/10.5067/MPYG15WAA4WX. 49: Copernicus Climate Change Service (C3S). "Global Temperature Trend Monitor." Accessed February 12, 2025. https://apps.climate.copernicus.eu/global-temperature-trend-monitor/ 50: Kleinen, T., S. Gromov, B. Steil and V. Brovkin Atmospheric methane underestimated in future climate projections. Environ. Res. Lett. 16, 094006 (2021). https://doi.org/10.1088/1748-9326/ac1814 Additional Declarations There is NO Competing Interest. Supplementary Files ExtendedData.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6097952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":424691054,"identity":"4bd6e96a-7331-4494-9f54-9813bb933c90","order_by":0,"name":"Masaki Toda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYJACxgYDEJXA+ICBQQJJlIAWoNIEZgOwFjaitICVJrBBrCCkRbf9+MOPMwoY6vjZk59V3dxhEc0v38C6ueAXg2w/Di1mZ3KMJTcAHSbZ88zsdu4ZidyZbQxst2f2MRjPxGGN2YEcBskHQC0GNxKAWtokcjccA2rh7WFI3HAAh5bzzx//hGhJ/1YM0rIfpmU/Li1Aw8EOM7iRY8YMtoUNqIXnB9AWXH658cbMcoaBhOTMnjfF0iC/zDiW2HZ7ZoOE8QycDkt/fLPnjw0/P3v6xs+5O+py+5sPH7td8MdGth+H96EAGumQuGBsYGZsk8CjGhnAoo+Z4Q+ROkbBKBgFo2AkAAB6UGE3pQTkXQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0770-1948","institution":"Max Planck Institute for Meteorology","correspondingAuthor":true,"prefix":"","firstName":"Masaki","middleName":"","lastName":"Toda","suffix":""},{"id":424691055,"identity":"1439bbaf-3125-4694-9b86-55f61223aeef","order_by":1,"name":"Tiffany Shaw","email":"","orcid":"https://orcid.org/0000-0002-0551-6810","institution":"The University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Tiffany","middleName":"","lastName":"Shaw","suffix":""},{"id":424691056,"identity":"780a5192-9820-4dae-98c8-d307d0c5e310","order_by":2,"name":"Sarah Kang","email":"","orcid":"https://orcid.org/0000-0003-4635-275X","institution":"Max Planck Institute for Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Kang","suffix":""}],"badges":[],"createdAt":"2025-02-24 14:51:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6097952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6097952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77933211,"identity":"661f5a40-9e35-44a4-be56-2ddd1646016a","added_by":"auto","created_at":"2025-03-07 03:31:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGHGs attribution of historical global-mean surface air temperature. \u003c/strong\u003eTime series of global-mean surface air temperature (SAT) anomaly relative to 1850-1900 from observations (see Methods) (black dashed line) and counterfactual model simulations: ALL (black solid line), CO2 (red solid line), CH4 (green solid line), other GHG(blue solid line). Colored shading represents the 5-95 percentile of the ensemble spread for each experiment. (b) Global mean SAT difference between the 2010-2019 mean and the 1850-1900 mean. The error bar represents the 5-95 percentile of the ensemble spread for each experiment. The cross represents observed values (see Methods for which observation data is used.). The filled black circle represents the sum of the ensemble means of CO2, CH4, and other GHG simulations. The black star indicates ensemble mean differences of ALL and all but CH4. Error bars indicate the 5-95 percentile of ensemble spread.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6097952/v1/2f766e34d7d490ac2bb53c4a.png"},{"id":77933208,"identity":"84255816-1545-4e80-bb11-1c028708004c","added_by":"auto","created_at":"2025-03-07 03:31:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGHG attribution of climate change indicators in the satellite era. \u003c/strong\u003eSatellite-era trends for (a) global-mean SAT (1979-2020), (b) land-mean maximum near-surface temperature (1979-2017), (c) Arctic sea ice fraction (1979-2020), and (d) global mean ocean heat content (1979-2018). The format is the same as in Fig. 1b.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6097952/v1/1213eba754088c1d701ee61a.png"},{"id":77933805,"identity":"50ba6281-e14e-4471-bad3-44c5d9d89226","added_by":"auto","created_at":"2025-03-07 03:39:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":244204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGHG attribution of warming over North America. \u003c/strong\u003e(a,d,g,j,m) Time series of North America mean SAT anomaly from 1850-1900 for each simulation (colored lines) and observation (black dashed line) (see Methods). The color shades indicate 5-95 percentile range of the ensemble members. The thick solid lines indicate linear regression lines of each experiment for 1979-2020. (b,e,h,k,n) Normalized probability distribution function (PDF) for the 1979–2020 trend ensemble members for each GHG contribution. The observed trend is indicated by the horizontal dashed line. The negative trend area is emphasized by colored shading. (c,f,i,l,o) Spatial pattern of ensemble mean SAT trend for 1979-2020 over North America for ALL, CO2, CH4 and other GHGs from the top to bottom.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6097952/v1/a41fca5020bd16a185dccd4a.png"},{"id":77933213,"identity":"81fc710d-4ebf-4d7f-add4-4e44fe10414b","added_by":"auto","created_at":"2025-03-07 03:31:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":285861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between GHG-induced global-mean warming and cumulative emissions, and the connection to decadal warming rate. \u003c/strong\u003e(a) Global mean surface air temperature (SAT) in observations (dashed black line) and counterfactual climate model simulations: ALL (black solid line), CO2 (red solid line) versus historical changes in cumulative CO\u003csub\u003e2\u003c/sub\u003e emission. The upper horizontal axis indicates the year corresponding to the cumulative emission. (c) As in (a), but for cumulative CH\u003csub\u003e4\u003c/sub\u003e emission and CH4 counterfactual simulation (green solid line). (e) Decadal warming rate defined as a 10-year linear trend of global mean SAT in counterfactual climate models simulations: All (black) and all but CH4 (purple). The x-axis corresponds to the start years of the 10-year trends. (b,d,f) As in (a,c,e), respectively, but for North America mean SAT. In all panels, the solid lines indicate the ensemble mean of each simulation and the color shading indicates the 5-95 percentile range of the ensemble spread.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6097952/v1/8096d87c10a956fec87fffc8.png"},{"id":78779223,"identity":"c2388674-8a1d-4ccf-a5e3-3a1685b12a52","added_by":"auto","created_at":"2025-03-18 19:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1464163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6097952/v1/41cf8991-4ff8-4e11-ad78-89ec2e8bf102.pdf"},{"id":77933209,"identity":"7ddea6f3-19ac-4bf2-8309-1675bfb9a7d5","added_by":"auto","created_at":"2025-03-07 03:31:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3912200,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-6097952/v1/91168591e736fcda973571bc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Minimal impact of methane on satellite-era regional climate change","fulltext":[{"header":"Main article","content":"\u003cp\u003eWell-mixed greenhouse gas (GHG) concentrations have increased significantly in the historical era since 1850 and the increase has been attributed to human activities\u003csup\u003e1\u003c/sup\u003e. Many physical climate changes (rising global-mean temperature, extreme heat, Arctic sea ice loss, etc) have been attributed to rising GHG concentrations in the historical (since 1850) and satellite (since 1979) eras\u003csup\u003e1\u003c/sup\u003e. As global warming continues unabated\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e and impacts accumulate, losses and damages are discussed\u003csup\u003e3\u003c/sup\u003e and mitigation options have been proposed for carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e)\u003csup\u003e4,5,6\u003c/sup\u003e and methane (CH\u003csub\u003e4\u003c/sub\u003e)\u003csup\u003e7,8,9,10,11\u003c/sup\u003e. It is important to attribute the contribution of specific GHGs to climate change indicators, including at the regional scale, in order to make effective policy decisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo approaches have emerged to link increased GHG concentrations to climate change indicators. Attribution studies use global climate models to simulate counterfactual worlds with or without the increase of anthropogenic GHG concentrations\u003csup\u003e12,13,14\u003c/sup\u003e. The attribution approach is the basis of quantifying human influence from GHGs on global and regional climate changes\u003csup\u003e1,15,16\u003c/sup\u003e. Radiative forcing studies use global climate models with fixed sea surface temperatures to estimate the radiative forcing due to increased GHG concentrations or emissions\u003csup\u003e17,18,19,20\u003c/sup\u003e, which is then used to estimate global warming\u003csup\u003e21,22,23,24\u003c/sup\u003e. Both approaches agree that GHGs have contributed 1.5 \u0026deg;C (1-2 \u0026deg;C for 90% range) to global warming between 1850-1900 and 2000-2019 (IPCC AR6 WG1 Fig. SPM2b,c)\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious assessments of the contribution of specific GHGs to historical global warming draw exclusively on radiative forcing studies\u003csup\u003e21,22,23,24,25\u003c/sup\u003e. More specifically, radiative forcing studies suggest CO\u003csub\u003e2\u003c/sub\u003e has contributed about half of the GHG-driven warming (~0.79 \u0026deg;C) since 1850, CH\u003csub\u003e4\u003c/sub\u003e contributes 0.51 \u0026deg;C (0.29-0.84 \u0026deg;C for 90% range) and other GHGs contribute 0.19 \u0026deg;C (IPCC AR6 Fig. SPM2c)\u003csup\u003e1\u003c/sup\u003e. Since 1750 the CH\u003csub\u003e4\u003c/sub\u003e contribution to global warming is roughly 28% of the CO₂ contribution considering the direct effect of prescribed concentration increase, but up to 67% if indirect effects, e.g., chemical feedbacks, are considered (IPCC AR6 Fig. 6.12, 7.7)\u003csup\u003e21,22\u003c/sup\u003e. While emissions-driven estimates allow for more complexity (chemical feedbacks\u003csup\u003e26\u003c/sup\u003e) there is considerable uncertainty in estimating emissions from observations\u003csup\u003e27,28\u003c/sup\u003e and chemical feedbacks themselves also entail uncertainty\u003csup\u003e29,30\u003c/sup\u003e. Regardless, previous assessments lend support to the idea of methane as an effective short-term mitigation option\u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRadiative forcing studies have several limitations. They assume fixed sea surface temperature\u003csup\u003e17,18,19,20\u003c/sup\u003e and subsequently either use a fixed climate feedback parameter or a simple emulator (e.g., 2-layer energy balance model)\u003csup\u003e21,22,23,24,25\u003c/sup\u003e. Thus, radiative forcing studies do not account for temporal changes in climate feedback parameters\u003csup\u003e31,32\u003c/sup\u003e and often do not account for chemical feedbacks\u003csup\u003e33\u003c/sup\u003e. In addition, radiative forcing studies only provide information about global warming; they do not provide information about other climate change indicators, including regional climate change. Finally, radiative forcing studies do not account for the impact of natural climate variability, resulting from atmosphere-ocean coupling\u003csup\u003e34\u003c/sup\u003e, which significantly impacts regional climate change\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRadiative forcing studies provide one line of evidence quantifying the contribution of individual GHGs to historical global warming. It is important to have multiple lines of evidence and assess the impact of individual GHGs across a broad suite of climate change indicators including regional climate change, which has not been reported before. It is especially important to do this for the satellite era where the rate of warming is higher than earlier in the historical era\u003csup\u003e36\u003c/sup\u003e and there are more observations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we follow the attribution approach of counterfactual single-forcing coupled global climate model simulations used extensively to attribute human influence from GHGs (IPCC WG1 Fig. SPM2b)\u003csup\u003e1,12,15\u003c/sup\u003e in order to quantify the impact of individual GHGs on climate change indicators beyond global warming, including for regional climate change. We quantify the impact of individual GHGs using single-forcing simulations in which CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e and other GHG concentrations change one at a time over the historical era (1850-2020, see Methods). We also consider a counterfactual world where everything but CH\u003csub\u003e4\u003c/sub\u003e changes. This concentration driven approach is the basis of all attribution statements of human influence from GHGs on global and regional climate change\u003csup\u003e1,12,15,16\u003c/sup\u003e. It is a natural intermediate step before moving to emission-driven simulations that may suffer from large uncertainty. We primarily focus on quantifying the contribution of different GHGs on climate change indicators, such as Arctic Sea ice concentration, maximum surface air temperature (SAT), ocean heat content, and regional warming during the satellite era. We also briefly examine their contribution to global warming during the historical era. We use a large-ensemble to compare the contribution of individual GHGs with the role of natural climate variability, which is known to significantly influence regional climate change\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAttribution of global-mean climate change to individual GHGs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observed global warming since 1850 is captured by the coupled global climate model simulations (compare solid and dashed black lines in Fig. 1a). Global warming exceeds the range of temperature change from natural variability and thus the signal is clearly distinguishable from the noise (black bars and whiskers do not overlap zero in Fig. 1b). As expected, CO\u003csub\u003e2\u003c/sub\u003e is the primary driver of historical global warming (compare black and red lines in Fig. 1a). The methane single forcing simulation shows that CH\u003csub\u003e4\u003c/sub\u003e is the second largest contributor among the GHGs, with impacts nearly comparable to the combined effect of all other GHGs (compare green and blue lines in Fig. 1a). Focusing on global-mean warming from 1850-1900 to 2010-2019, CO\u003csub\u003e2\u003c/sub\u003e contributes 0.8 \u0026deg;C, CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003econtributes 0.2 \u0026deg;C and other GHGs contribute 0.26 \u0026deg;C (Fig. 1b). Methane and other GHGs\u0026rsquo; global warming contribution is 24% and 31% of that of carbon dioxide during this time period, respectively. Across different time periods, methane\u0026rsquo;s global warming contribution ranges between 25% and 40% of that from carbon dioxide (Extended Data Fig. 1a). The above results remain unchanged whether methane\u0026apos;s contribution is derived from the methane single forcing experiment or by subtracting the all-but-CH₄ forcing simulation from the all-forcing simulation (compare green bar and the star marker in Fig. 1b, Extended Data Fig. 1b), indicating the small nonlinearity of methane.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious assessments based on radiative forcing studies have only quantified the contribution of different GHGs to global warming since 1850 or 1750. Reliable observations of other global warming indicators (global-land-mean maximum SAT, Arctic sea ice fraction, and global-mean ocean heat content) have only emerged in the satellite era since 1979, which corresponds to a period of accelerated global warming (Fig. 1a). The all-forcing simulation captures the evolution of these global warming indicators in the satellite era (compare black bars and crosses in Fig. 2). The single forcing simulations indicate that during the satellite era, CO\u003csub\u003e2\u003c/sub\u003e contributes 67% of the global warming (compare black and red bars, Fig. 2a). CH\u003csub\u003e4\u003c/sub\u003e is 16% of the CO\u003csub\u003e2\u003c/sub\u003e contribution (green bar, Fig. 2a), smaller than the contribution from all other GHGs which represent 33% of the CO\u003csub\u003e2\u003c/sub\u003e contribution (blue bar, Fig. 2a).\u003c/p\u003e\n\u003cp\u003eThe contribution of individual GHGs to other global warming indicators, including global-land-mean maximum SAT (see Methods), Arctic sea ice fraction, and global-mean ocean heat content are very similar to those for global warming (Fig. 2b-d, Extended Data Fig. 2b-d). Namely, CO\u003csub\u003e2\u003c/sub\u003e is the dominant contributor, accounting for roughly 60-66% of the total change. Methane contributes approximately 21-26% of the CO\u003csub\u003e2\u003c/sub\u003e contribution, and other GHGs contribute approximately 31-34% of the CO\u003csub\u003e2\u003c/sub\u003e contribution. Across all climate change indicators the CO\u003csub\u003e2\u003c/sub\u003e contribution exceeds the noise (red bar and whiskers are not overlapping zero, Fig. 2). However, the contribution of methane and other GHGs are not detectable given the noise (green and blue bars and whiskers are overlapping zero, Fig. 2) with the exception of ocean heat content. This is likely because ocean heat content is governed by the deep ocean, making it less sensitive to natural variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, during the satellite era, CO\u003csub\u003e2\u003c/sub\u003e dominates the change in all global warming indicators with methane the second largest contribution. Methane\u0026rsquo;s contribution is smaller than that for the historical era and is negligible compared to natural variability, which may be linked to the stagnation of atmospheric CH\u003csub\u003e4\u003c/sub\u003e concentrations since 1985\u0026nbsp;(Extended Data Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAttribution of satellite-era warming over North America to individual GHGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe counterfactual simulations allow us to attribute the contribution of different GHGs to regional warming, which has emerged in the satellite era. Here we focus on warming over North America following previous work\u003csup\u003e35\u003c/sup\u003e but similar results are found for other continents (Extended Data Fig. 4). Focusing on regional warming is important because it is in these scales where the impacts of climate change are most directly experienced.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe satellite-era warming over North America is captured by the all-forcing simulation (compare solid and dashed black lines in Fig. 3a). The distribution of satellite era trends across all ensemble members is well separated from zero (Fig. 3b), consistent with the signal dominating over the noise of natural variability. The spatial distribution of the ensemble mean trend shows warming across the entire North American continent (Fig. 3c). However, the spatial pattern of ensemble members with the highest and lowest warming trends in the ensemble vary significantly, indicating an important role for natural variability (Extended Data Fig. 5).\u003c/p\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e dominates the warming over North America in the satellite era with a clear positive trend since 1979 (Fig. 3d). The contribution of CO\u003csub\u003e2\u003c/sub\u003e accounts for 61% of the warming over North America in the satellite era.\u0026nbsp;The distribution of satellite era trends for all CO2 members exhibit a positive trend, demonstrating that CO\u003csub\u003e2\u003c/sub\u003e-induced warming is robust and contributes significantly to warming across the North American continent (Fig. 3f)\u003c/p\u003e\n\u003cp\u003eThe contribution of CH\u003csub\u003e4\u003c/sub\u003e and other GHGs to warming over North America is minimal in the satellite era, appearing nearly flat in the time series (green and blue solid lines in Fig. 3g,j). For both CH\u003csub\u003e4\u003c/sub\u003e and other GHGs, 22.5% of the ensemble members involve a cooling trend (green and blue curves in Fig. 3h,k). Thus the impact of methane and other GHGs for continental-scale warming is difficult to detect. Consistently, the distribution of trends in response to CO\u003csub\u003e2\u003c/sub\u003e forcing is statistically different from those of CH\u003csub\u003e4\u003c/sub\u003e (p value \u0026lt; 0.001) and other GHGs (p value \u0026lt; 0.001) (see Methods). Methane and other GHGs also do not significantly contribute to the spatial pattern of warming over North America (Fig. 3i,l).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe limited contribution of methane is further confirmed by examining the counterfactual world where all but CH\u003csub\u003e4\u003c/sub\u003e changes. Despite the complete absence of methane, North America robustly warms (Fig. 3m). The distribution of satellite era trends across all ensemble members with all forcings and all but CH\u003csub\u003e4\u003c/sub\u003e largely overlap (black and purple curves, Fig. 3n), indicating a broad similarity. Furthermore, the spatial patterns of warming at the continental scale for all forcings and all but CH4 are nearly indistinguishable (Fig. 3o).\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor other climate change indicators such as Arctic sea ice and maximum SAT over North America, CO\u003csub\u003e2\u003c/sub\u003e also dominates and the contributions of methane and other GHGs are not significant compared to natural variability (Extended Data Figs. 6 and 7). In particular, as many as 37.5% of the CH4 members show increased Arctic sea ice fraction in the satellite era, consistent with it being highly influenced by natural variability\u003csup\u003e37\u003c/sup\u003e. Overall, CO\u003csub\u003e2\u003c/sub\u003e plays a dominant role in the warming over North America and other continents (Extended Data Fig. 4) as well as other climate change indicators during the satellite era. Conversely, the contributions of methane and other GHGs are negligible and, importantly, indistinguishable from natural climate variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for GHG mitigation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe attribution results indicate that CO\u003csub\u003e2\u003c/sub\u003e has been the dominant driver of all climate change indicators from 1850 to 2020. The dominant contribution of CO\u003csub\u003e2\u003c/sub\u003e can be also highlighted by comparing global warming and the cumulative CO₂ emissions. Global warming is proportional to cumulative CO₂ emissions during the historical era for both all forcing and the CO\u003csub\u003e2\u003c/sub\u003e single forcing simulation\u003csup\u003e38,39\u003c/sup\u003e (Fig. 4a). This linear relationship holds even for warming over North America (Fig. 4b). These results lend further support to the idea that carbon dioxide removal (CDR) is an effective way to mitigate global and regional warming.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethane has also been proposed as a means to mitigate global warming, especially on short time scales. However, our attribution results based on single forcing simulations indicate that methane makes a small contribution to historical and satellite era climate change (Fig. 1, 2a-c and 3g). Consistently, historical global and regional warming do not scale linearly with methane emissions (black line, Fig. 4c,d). Although the CH\u003csub\u003e4\u003c/sub\u003e contribution to global and regional warming from the single forcing CH\u003csub\u003e4\u003c/sub\u003e simulation does scale linearly with cumulative methane emissions, its warming contribution is minimal (green line, Fig. 4c,d). These results suggest that methane mitigation has a limited effect on warming over multi-decadal or longer timescales.\u003c/p\u003e\n\u003cp\u003eMethane has been proposed as an attractive mitigation option because of its short atmospheric lifetime of approximately 10 years\u003csup\u003e40\u003c/sup\u003e, which is significantly shorter than CO\u003csub\u003e2\u003c/sub\u003e. However, the decadal warming rates in the all forcing and all but CH\u003csub\u003e4\u003c/sub\u003e simulations are nearly unaffected by the absence of CH\u003csub\u003e4\u003c/sub\u003e, suggesting that methane\u0026rsquo;s contribution is limited even on decadal timescales (Fig. 4d,e). This implies that the effects of methane mitigation may be difficult to detect even on a decadal timescale due to the influence of natural variability and natural forcing (Extended Data Fig. 8).\u003c/p\u003e\n\u003cp\u003eFinally, CO\u003csub\u003e2\u003c/sub\u003e and methane have both been proposed as mitigation options in the context of the 1.5 \u0026deg;C target set out by the Paris Agreement. In the all-forcing simulation, the ensemble mean reaches 1.5 \u0026deg;C in July 2033 (Methods, Extended Data Fig. 9). While this timing is delayed by 8 years in the counterfactual world excluding CH\u003csub\u003e4\u003c/sub\u003e, in terms of the ensemble mean (Methods, Extended Data Fig. 9), the time of emergence across ensembles exhibits a considerable overlap between the two simulations. Given that the all but CH\u003csub\u003e4\u003c/sub\u003e simulation represents an extreme mitigation scenario, which is unlikely to be achieved in reality, the results suggest the contribution of realistic methane mitigation efforts would be minimal in delaying the 1.5 \u0026deg;C threshold.\u003c/p\u003e"},{"header":"Summary and Discussion","content":"\u003cp\u003eTo date the only line of evidence quantifying the contributions of individual GHGs to climate change indicators comes from radiative forcing studies focused on global warming. Here, we conduct counterfactual global climate model simulations to attribute historical and satellite-era changes in SAT, maximum near-surface temperature, Arctic sea ice fraction, and ocean heat content to individual GHGs at both global and regional scales. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the historical era (1850-2019), CO\u003csub\u003e2\u003c/sub\u003e contributed approximately 0.80 °C of global warming, while CH\u003csub\u003e4\u003c/sub\u003e and other GHGs contributed around 0.20 °C and 0.26 °C, respectively, making methane's impact about 25% of that of CO\u003csub\u003e2\u003c/sub\u003e. However, in the satellite era (since 1979), methane's contribution declined further, representing only 16–24% of CO\u003csub\u003e2\u003c/sub\u003e’s effect on climate change indicators, and these signals are not detectable given natural variability. For regional warming at the continental scale CO\u003csub\u003e2\u003c/sub\u003e remains the dominant contributor, while the contribution of methane and other GHGs to the climate change signal does not exceed the noise.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dominance of CO\u003csub\u003e2\u003c/sub\u003e for global and regional climate change across all climate change indicators supports CO\u003csub\u003e2\u003c/sub\u003e mitigation efforts like CDR. However, our results suggest that the effectiveness of methane mitigation is very limited not only on multi-decadal time scales but also on decadal time scales. This is because natural variability and natural forcing have a greater influence on decadal warming rates than methane. Furthermore, the results suggest methane reduction would delay crossing the 1.5 °C threshold by only a few years at most.\u003c/p\u003e\n\u003cp\u003eOur attribution study results for global warming in the historical era are in good agreement with the results of radiative forcing studies. Thus multiple lines of evidence support the dominance of carbon dioxide with methane as the second largest contribution across all climate change indicators. The contribution of individual GHGs may be sensitive to the inclusion of feedbacks from biology (plant stomatal resistance) and atmospheric chemistry (tropospheric ozone\u003csup\u003e41\u003c/sup\u003e, and stratospheric water vapor\u003csup\u003e42\u003c/sup\u003e), which require emissions driven simulations that are more uncertain. Here, as a first step, we focused on a concentration driven approach, which is the basis of attribution studies that have quantified the human influence of GHGs on various climate change indicators\u003csup\u003e1,15,16\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Thomas Radattz for his contributions to conducting many of the numerical experiments and for providing valuable guidance to M.T. in carrying them out. This work was supported by the National Science Foundation (grant AGS-2300037 to T.A.S.) and the National Oceanic Atmospheric Administration (grant NA23OAR4310597 to T.A.S.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.A.S. initiated the study. M.T., S.K., and T.A.S. designed the model experiments.. M.T. conducted all of the analysis and wrote the manuscript with feedback from S.K. and T.A.S.. All authors discussed the results and provided feedback on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting financial interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIPCC Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2021);\u0026nbsp; doi:10.1017/9781009157896.001.\u003c/li\u003e\n\u003cli\u003eNASA. Temperatures Rising: NASA Confirms 2024 Warmest Year on Record. https://www.nasa.gov/news-release/temperatures-rising-nasa-confirms-2024-warmest-year-on-record/ (2024).\u003c/li\u003e\n\u003cli\u003eIPCC Summary for Policymakers In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC 2022); doi:10.1017/9781009325844.001.\u003c/li\u003e\n\u003cli\u003eAchakulwisut, P., Erickson, P., Guivarch, C. et al. Global fossil fuel reduction pathways under different climate mitigation strategies and ambitions. Nat Commun \u003cstrong\u003e14\u003c/strong\u003e, 5425 (2023). https://doi.org/10.1038/s41467-023-41105-z\u003c/li\u003e\n\u003cli\u003eOyewole, K. A., Okedere, O. B., Rabiu, K. O., Alawode, K. O., \u0026amp; Oyelami, S. Carbon dioxide emission, mitigation and storage technologies pathways. Sustainable Environment \u003cstrong\u003e9,\u003c/strong\u003e (2023). https://doi.org/10.1080/27658511.2023.2188760\u003c/li\u003e\n\u003cli\u003eNational Academies of Sciences, Engineering, and Medicine. Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. Washington, DC: The National Academies Press (2019). https://doi.org/10.17226/25259\u003c/li\u003e\n\u003cli\u003eNational Academies of Sciences, Engineering, and Medicine. A Research Agenda Toward Atmospheric Methane Removal. Washington, DC: The National Academies Press (2024). https://doi.org/10.17226/27157\u003c/li\u003e\n\u003cli\u003eNisbet, E. G., Fisher, R. E., Lowry, D., France, J. L., Allen, G., Bakkaloglu, S., et al. Methane mitigation: methods to reduce emissions, on the path to the Paris agreement. Reviews of Geophysics \u003cstrong\u003e58\u003c/strong\u003e, (2020). https://doi.org/10.1029/2019RG000675\u003c/li\u003e\n\u003cli\u003eArndt, A.N. Hristov, W.J. Price, S.C. McClelland, A.M. Pelaez, S.F. Cueva, J. Oh, J. Dijkstra, A. Bannink, A.R. Bayat Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 C target by 2030 but not 2050 Proc. Natl. Acad. Sci., 119 (2022), Article e2111294119.\u003c/li\u003e\n\u003cli\u003e Global Methane Pledge. *Global Methane Pledge*. https://www.globalmethanepledge.org/ (accessed 25 Jan. 2025).\u003c/li\u003e\n\u003cli\u003eDrew Shindell et al. Simultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food Security. Science \u003cstrong\u003e335\u003c/strong\u003e, 183-189 (2012) https://doi.org/10.1126/science.1210026.\u003c/li\u003e\n\u003cli\u003e Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., Santer, B. D., Stone, D., and Tebaldi, C.: The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6, Geosci. Model Dev. \u003cstrong\u003e9\u003c/strong\u003e, 3685\u0026ndash;3697 (2016), https://doi.org/10.5194/gmd-9-3685-2016\u003c/li\u003e\n\u003cli\u003e Gillett, N.P., Kirchmeier-Young, M., Ribes, A. et al. Constraining human contributions to observed warming since the pre-industrial period. Nat. Clim. Chang. \u003cstrong\u003e11\u003c/strong\u003e, 207\u0026ndash;212 (2021). https://doi.org/10.1038/s41558-020-00965-9\u003c/li\u003e\n\u003cli\u003e Aur\u0026eacute;lien Ribes et al. ,Making climate projections conditional on historical observations.Sci. Adv. \u003cstrong\u003e7\u003c/strong\u003e, eabc0671 (2021). https://doi.org/10.1126/sciadv.abc0671\u003c/li\u003e\n\u003cli\u003e Eyring, V., et al. Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 423\u0026ndash;552 (2021). https://doi.org/10.1017/9781009157896.005\u003c/li\u003e\n\u003cli\u003e Deser, C., et al. Isolating the Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New CESM1 Large Ensemble Community Resource. Journal of Climate \u003cstrong\u003e33\u003c/strong\u003e, 7835\u0026ndash;58 (2020). https://doi.org/10.1175/JCLI-D-20-0123.1\u003c/li\u003e\n\u003cli\u003e Pincus, R., Forster, P. M., and Stevens, B. The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6, Geosci. Model Dev. \u003cstrong\u003e9\u003c/strong\u003e, 3447\u0026ndash;3460 (2016). https://doi.org/10.5194/gmd-9-3447-2016\u003c/li\u003e\n\u003cli\u003e Smith, C. J., Kramer, R. J., Myhre, G., Forster, P. M., Soden, B. J., Andrews, T., et al. Understanding rapid adjustments to diverse forcing agents. Geophysical Research Letters, \u003cstrong\u003e45\u003c/strong\u003e, 12,023\u0026ndash;12,031 (2018). https://doi.org/10.1029/2018GL079826\u003c/li\u003e\n\u003cli\u003e Smith, C.J. et al., Effective radiative forcing and adjustments in CMIP6 models. Atmospheric Chemistry and Physics \u003cstrong\u003e20\u003c/strong\u003e(16), 9591\u0026ndash;9618 (2020). https://doi.org/10.5194/acp-20-9591-2020.\u003c/li\u003e\n\u003cli\u003e Thornhill, G. D. et al. Effective radiative forcing from emissions of reactive gases and aerosols\u0026ndash;a multi-model comparison. Atmos. Chem. Phys. \u003cstrong\u003e21\u003c/strong\u003e, 853\u0026ndash;874 (2021). https://doi.org/10.5194/acp-21-853-2021\u003c/li\u003e\n\u003cli\u003e Forster, P., T. et al. The Earth\u0026rsquo;s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 923\u0026ndash;1054 (2021) https://doi.org/10.1017/9781009157896.009\u003c/li\u003e\n\u003cli\u003e Szopa, S. et al. Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 817\u0026ndash;922 (2021). https://doi.org/10.1017/9781009157896.008\u003c/li\u003e\n\u003cli\u003e Leach, N. J., Jenkins, S., Nicholls, Z., Smith, C. J., Lynch, J., Cain, M., Walsh, T., Wu, B., Tsutsui, J., and Allen, M. R. FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration. Geosci. Model Dev. \u003cstrong\u003e14\u003c/strong\u003e, 3007\u0026ndash;3036 (2021). https://doi.org/10.5194/gmd-14-3007-2021\u003c/li\u003e\n\u003cli\u003e Nicholls, Z. R. J., et al. Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response. Geosci. Model Dev. \u003cstrong\u003e13\u003c/strong\u003e, 5175\u0026ndash;5190, (2020). https://doi.org/10.5194/gmd-13-5175-2020\u003c/li\u003e\n\u003cli\u003e Cain, M., Lynch, J., Allen, M.R. et al. Improved calculation of warming-equivalent emissions for short-lived climate pollutants. npj Clim Atmos Sci \u003cstrong\u003e2\u003c/strong\u003e, 29 (2019). https://doi.org/10.1038/s41612-019-0086-4\u003c/li\u003e\n\u003cli\u003e Collins, W. J. et al. AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6, Geosci. Model Dev. \u003cstrong\u003e10\u003c/strong\u003e, 585\u0026ndash;607 (2017). https://doi.org/10.5194/gmd-10-585-2017\u003c/li\u003e\n\u003cli\u003e Solazzo, E., Crippa, M., Guizzardi, D., Muntean, M., Choulga, M., and Janssens-Maenhout, G. Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases, Atmos. Chem. Phys. \u003cstrong\u003e21\u003c/strong\u003e, 5655\u0026ndash;5683 (2021). https://doi.org/10.5194/acp-21-5655-2021\u003c/li\u003e\n\u003cli\u003e Rosentreter, J. A., Alcott, L., Maavara, T., Sun, X., Zhou, Y., Planavsky, N. J., \u0026amp; Raymond, P. A. Revisiting the global methane cycle through expert opinion. Earth's Future \u003cstrong\u003e12\u003c/strong\u003e, e2023EF004234, (2024). https://doi.org/10.1029/2023EF004234\u003c/li\u003e\n\u003cli\u003e Thornhill, G. et al. Climate-driven chemistry and aerosol feedbacks in CMIP6 Earth system models, Atmos. Chem. Phys. \u003cstrong\u003e21\u003c/strong\u003e, 1105\u0026ndash;1126 (2021). https://doi.org/10.5194/acp-21-1105-2021\u003c/li\u003e\n\u003cli\u003e Heimann, I., Griffiths, P. T., Warwick, N. J., Abraham, N. L., Archibald, A. T., \u0026amp; Pyle, J. A. Methane emissions in a chemistry-climate model: Feedbacks and climate response. Journal of Advances in Modeling Earth Systems \u003cstrong\u003e12\u003c/strong\u003e, e2019MS002019 (2020). https://doi.org/10.1029/2019MS002019\u003c/li\u003e\n\u003cli\u003e Andrews, T., J. M. Gregory, and M. J. Webb The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models. J. Climate \u003cstrong\u003e28\u003c/strong\u003e, 1630\u0026ndash;1648 (2015). https://doi.org/10.1175/JCLI-D-14-00545.1.\u003c/li\u003e\n\u003cli\u003e Armour, K. C., C. M. Bitz, and G. H. Roe Time-Varying Climate Sensitivity from Regional Feedbacks. J. Climate \u003cstrong\u003e26\u003c/strong\u003e, 4518\u0026ndash;4534 (2013). https://doi.org/10.1175/JCLI-D-12-00544.1.\u003c/li\u003e\n\u003cli\u003e Richardson, T.B., et al. Efficacy of climate forcings in PDRMIP models. J. Geophys. Res. Atmos. \u003cstrong\u003e124\u003c/strong\u003e, no. 23, 12824-12844 (2019). https://doi.org/10.1029/2019JD030581.\u003c/li\u003e\n\u003cli\u003e Battisti, D. S., D. J. Vimont, and B. P. Kirtman 100 Years of Progress in Understanding the Dynamics of Coupled Atmosphere\u0026ndash;Ocean Variability. Meteor. Monogr. \u003cstrong\u003e59\u003c/strong\u003e, 8.1\u0026ndash;8.57 (2019). https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0025.1.\u003c/li\u003e\n\u003cli\u003e Deser, C., Knutti, R., Solomon, S. et al. Communication of the role of natural variability in future North American climate. Nature Clim Change \u003cstrong\u003e2\u003c/strong\u003e, 775\u0026ndash;779 (2012). https://doi.org/10.1038/nclimate1562\u003c/li\u003e\n\u003cli\u003e NASA Earth Observatory. (n.d.). World of Change: Global Temperatures. NASA. Retrieved February 7, 2025, from https://earthobservatory.nasa.gov/world-of-change/global-temperatures\u003c/li\u003e\n\u003cli\u003e England, M., A. Jahn, and L. Polvani Nonuniform Contribution of Internal Variability to Recent Arctic Sea Ice Loss. J. Climate \u003cstrong\u003e32\u003c/strong\u003e, 4039\u0026ndash;4053 (2019). https://doi.org/10.1175/JCLI-D-18-0864.1.\u003c/li\u003e\n\u003cli\u003e Matthews, H., Gillett, N., Stott, P. et al. The proportionality of global warming to cumulative carbon emissions. Nature \u003cstrong\u003e459\u003c/strong\u003e, 829\u0026ndash;832 (2009). https://doi.org/10.1038/nature08047\u003c/li\u003e\n\u003cli\u003e Canadell, J.G. Global Carbon and other Biogeochemical Cycles and Feedbacks. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 673\u0026ndash;816 (2021). https://doi.org/10.1017/9781009157896.007.\u003c/li\u003e\n\u003cli\u003e Prather, M. J., Holmes, C. D., \u0026amp; Hsu, J. Reactive greenhouse gas scenarios: Systematic exploration of uncertainties and the role of atmospheric chemistry. Geophysical Research Letters, \u003cstrong\u003e39\u003c/strong\u003e(9), L09803 (2012). https://doi.org/10.1029/2012GL051440\u003c/li\u003e\n\u003cli\u003e Fiore, A. M., D. J. Jacob, B. D. Field, D. G. Streets, S. D. Fernandes, and C. Jang Linking ozone pollution and climate change: The case for controlling methane, Geophys. Res. Lett., \u003cstrong\u003e29\u003c/strong\u003e(19), 1919 (2002). https://doi.org/10.1029/2002GL015601\u003c/li\u003e\n\u003cli\u003e Charlesworth, E., Pl\u0026ouml;ger, F., Birner, T. et al. Stratospheric water vapor affecting atmospheric circulation. Nat Commun \u003cstrong\u003e14\u003c/strong\u003e, 3925 (2023). https://doi.org/10.1038/s41467-023-39559-2002019\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoupled Climate Model simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use MPI-ESM1.2-LR\u003csup\u003e43\u003c/sup\u003e for conducting the series of experiments. This version of the Earth System Model has also been utilized in the Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth System Model \u0026nbsp;consisting of the atmospheric model ECHAM6.3, the ocean model MPIOM1.6, the ocean biogeochemistry model HAMOCC6, and the land surface model JSBACH3.2. The atmosphere has 47 vertical levels with a horizontal resolution of \u0026nbsp;approximately 200 km. The ocean model has 40 vertical levels and a horizontal resolution of approximately 150 km.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we make use of 50 historical simulations (ALL) driven by the CMIP6 suite of historical forcing, including concentrations of long-lived GHGs (CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, chlorofluorocarbons (CFCs)), anthropogenic aerosols, ozone, land use change and natural forcing (stratospheric aerosol, solar irradiance, orbital changes). We conducted 40 member single forcing simulations where all GHGs evolve (denoted GHG), which is the standard GHG attribution simulation\u003csup\u003e12\u003c/sup\u003e. This is further divided into single-forcing GHG simulations: CO2, CH4, and other GHG (N2O plus CFCs), respectively, are varied while all other GHGs are held constant at 1850 levels. The responses to single-forcing GHG simulations sum approximately to the response to all-GHG simulation (compare filled black circle and orange bar in Figs. 1b and 2, Extended Data Fig. 1c). This near-linearity allows us to attribute the climate response to each GHG forcing. To estimate the methane\u0026rsquo;s contribution more closely in particular with respect to nonlinear responses in climate, we conducted simulations with all historical forcing but methane (all but CH4). Finally, we also performed anthropogenic aerosols only (AER) and natural forcing only (NAT) experiments with 30 members, where anthropogenic aerosols and natural forcings are varied respectively, while everything else is fixed at pre-industrial levels. See Extended Data Table 1 for an overview of the ensembles and the forcing applied for each of them.\u003c/p\u003e\n\u003cp\u003eAll simulations are integrated for the period 1850-2020, following the CMIP6 historical simulation protocol for 1850\u0026ndash;2014 and the SSP245 scenario for the last six years. Regarding maximum near surface temperature, only 30 members are available for CO2, CH4, other, GHG, and all but CH4 for a technical reason.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObservation datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor observed surface air temperature, we use the average of three data sets, Berkeley Earth\u003csup\u003e44\u003c/sup\u003e, NOAAGlobalTemp version 6.0.0\u003csup\u003e45\u003c/sup\u003e, and HadCRUT5.0.2.0\u003csup\u003e46\u003c/sup\u003efrom 1850-2020. For land mean maximum near surface temperature observations, we use the annual maximum of daily maximum from HadEX3\u003csup\u003e47\u0026nbsp;\u003c/sup\u003efrom 1978-2017 (TXx annual). \u0026nbsp;For Arctic sea ice fraction observations, we use Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 2\u003csup\u003e48\u003c/sup\u003e from 1978\u0026ndash;2020. For ocean heat content, we use the best estimate of IPCC AR6 WG1 Fig. 3.26\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation of the timing of 1.5 \u0026deg;C warming since the pre-industrial era\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe timing of 1.5 \u0026deg;C warming since the pre-industrial era is determined by linearly extrapolating the monthly global mean SAT anomaly time series\u003csup\u003e49\u003c/sup\u003e. For each ensemble member of ALL and all but CH₄, the 1850\u0026ndash;1900 monthly climatology is computed and subtracted from the monthly global mean SAT time series to obtain the monthly SAT anomaly time series. The timing of 1.5 \u0026deg;C is then determined by linearly extrapolating this monthly anomaly time series. The extrapolation is based on the 360-month time series from January 1991 to December 2020. The ensemble mean timing for ALL and all but CH₄ scenarios is obtained by first computing the ensemble mean SAT anomaly time series, and then performing the extrapolation. It is not derived by averaging the timing of individual ensemble members.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of maximum near surface temperature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, maximum near surface temperature is defined as the annual maximum of the daily maximum near surface temperature. For the model output, the highest temperature at each time step within a day is extracted as daily data, and the highest temperature of the year is then identified. Since HadEX3 contains missing values in some regions, when calculating the regional average of maximum near surface temperature of model simulations, areas with missing values in HadEX3 are masked out on the model grid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative methane emission\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCumulative methane emissions are calculated from 1850 onward using annual natural methane emissions and anthropogenic methane emissions from Kleinen et al. (2021)\u003csup\u003e50\u003c/sup\u003e (see their Figures 2 and 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA significance test is performed for the difference between ensemble means of two experiments with Welch\u0026rsquo;s t-test (two-tailed), where the static value \u003cem\u003et\u003c/em\u003e is defined as\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 291px;\"\u003e\u003c/p\u003e\n\u003cp\u003eHere, \u0026nbsp;\u003cem\u003eX̅\u003c/em\u003e is the average of a sample, \u003cem\u003en\u003c/em\u003e is the sample size, and \u003cem\u003es\u003c/em\u003e is the corrected sample standard deviation. Subscripts 1,2 denote different samples. The test uses a \u003cem\u003et\u003c/em\u003e-distribution with integer degrees of freedom that is closest to\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 360px;\"\u003e\u003c/p\u003e\n\u003cp\u003eIf members in a sample can be assumed to be independent, for example in a test of the difference between the ensemble means of different two experiments, assign the sample size to \u003cem\u003en\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and code availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBerkeley Earth is available at \u0026nbsp;https://berkeleyearth.org/data/. NOAAGlobalTemp version6.0.0 is available at https://www.ncei.noaa.gov/data/noaa-global-surface-temperature/v6/access/. HadCRUT5.0.2.0 is available at https://www.metoffice.gov.uk/hadobs/hadcrut5/data/HadCRUT.5.0.2.0/download.html. HadEX3 annual TXx is available at https://data.ceda.ac.uk/badc/ukmo-hadobs/data/derived/MOHC/HadOBS/HadEX3/v3-0-4/. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 2 is available at https://nsidc.org/data/nsidc-0051/versions/2. The best estimate of observed ocean heat content by IPCC AR6 is available at https://catalogue.ceda.ac.uk/uuid/85168e39bfff444ba02bf55e7682f73d/. \u0026nbsp; Cumulative CO\u003csub\u003e2\u003c/sub\u003e emission since 1850 is available at https://catalogue.ceda.ac.uk/uuid/cfe938e70f8f4e98b0622296743f7913/. The natural CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003eemission is available at https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_060_ds00007. The anthropogenic CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003eemission is available at https://aims2.llnl.gov/search/input4MIPs/.\u003c/p\u003e\n\u003cp\u003eThe data archiving of the fully coupled experiments using MPI-ESM is underway. The archiving of python codes used for creating the figures in this study are also underway. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods-only references\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e43: Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., et al. Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems \u003cstrong\u003e11\u003c/strong\u003e, 998\u0026ndash;1038 (2019). https://doi.org/10.1029/2018MS001400\u003c/p\u003e\n\u003cp\u003e44: Rohde, R. A., and Z. Hausfather The Berkeley Earth Land/Ocean Temperature Record. Earth Syst. Sci. Data \u003cstrong\u003e12\u003c/strong\u003e, 3469\u0026ndash;3479, (2020). \u0026nbsp; https://doi.org/10.5194/essd-12-3469-2020\u003c/p\u003e\n\u003cp\u003e45: Huang, Boyin; Yin, Xungang; Menne, Matthew J.; Vose, Russell S.; and Zhang, Huai-Min. NOAA Global Surface Temperature Dataset (NOAAGlobalTemp), Version 6.0.0. NOAA National Centers for Environmental Information (2024). https://doi.org/10.25921/rzxg-p717\u003c/p\u003e\n\u003cp\u003e46: Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., et al. An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set. Journal of Geophysical Research: Atmospheres \u003cstrong\u003e126\u003c/strong\u003e, e2019JD032361 (2021). https://doi.org/10.1029/2019JD032361.\u003c/p\u003e\n\u003cp\u003e47: Dunn, R. J. H., et al. Observed global changes in sector-relevant climate extremes indices - an extension to HadEX3, ESS \u003cstrong\u003e11\u003c/strong\u003e, e2023EA003279 (2024). https://doi.org/10.1029/2023EA003279\u003c/p\u003e\n\u003cp\u003e48: DiGirolamo, N., Parkinson, C. L., Cavalieri, D. J., Gloersen, P. \u0026amp; Zwally, H. J. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. (NSIDC-0051, Version 2). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center (2022). https://doi.org/10.5067/MPYG15WAA4WX.\u003c/p\u003e\n\u003cp\u003e49: Copernicus Climate Change Service (C3S). \u0026quot;Global Temperature Trend Monitor.\u0026quot; Accessed February 12, 2025. https://apps.climate.copernicus.eu/global-temperature-trend-monitor/\u003c/p\u003e\n\u003cp\u003e50: Kleinen, T., S. Gromov, B. Steil and V. Brovkin Atmospheric methane underestimated in future climate projections. Environ. Res. Lett. 16, 094006 (2021). https://doi.org/10.1088/1748-9326/ac1814\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6097952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6097952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The attribution of global and regional climate change to anthropogenic greenhouse gases (GHGs) is well appreciated. Existing estimates based on radiative forcing studies suggest carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) has dominated global warming since 1850 with methane (CH\u003csub\u003e4\u003c/sub\u003e) the second largest contribution. However, radiative forcing studies involve several assumptions and the attribution of GHGs contributions for other climate change indicators is unknown. Here we quantify the impact of individual GHGs on climate change indicators, including regional climate change, in the satellite era using an attribution approach of counterfactual single-forcing CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, and other GHGs coupled climate model simulations. CO\u003csub\u003e2\u003c/sub\u003e dominates global warming, Arctic Sea ice loss, extreme temperatures and regional warming over North America in the satellite era with CH\u003csub\u003e4\u003c/sub\u003e and other GHGs contributing merely around 20% and 30% of the CO\u003csub\u003e2\u003c/sub\u003e contribution, respectively. The results demonstrate that, on multi-decadal or longer time scales, CO\u003csub\u003e2\u003c/sub\u003e dominates and the contribution of CH\u003csub\u003e4\u003c/sub\u003e and other GHGs is small and not distinguishable from noise, especially for regional climate changes. Thus, CH\u003csub\u003e4\u003c/sub\u003e mitigation may not be as effective as previously thought, particularly for regional scale impacts.","manuscriptTitle":"Minimal impact of methane on satellite-era regional climate change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-07 03:31:48","doi":"10.21203/rs.3.rs-6097952/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d664a543-f395-4cc4-a81e-9378a59301e0","owner":[],"postedDate":"March 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45261134,"name":"Earth and environmental sciences/Climate sciences/Climate change/Attribution"},{"id":45261135,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"}],"tags":[],"updatedAt":"2025-03-18T19:00:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-07 03:31:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6097952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6097952","identity":"rs-6097952","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}