| 06 Nov 2025
Quantification of the influence of anthropogenic and natural factors on the record-high temperatures in 2023 and 2024
Abstract. The unexpectedly high global mean surface temperature (GMST) anomalies in 2023 and 2024 greatly exceeded the temperatures observed in the years directly prior. In this paper, we use a multiple linear regression energy balance model to quantify the contributions of several natural and anthropogenic factors to the GMST, including the large reduction of sulfur emissions from the shipping sector since 2020. The model is trained on 170 years of historical climate data, and allows for the attribution of warming to various natural and anthropogenic factors. The influence of anthropogenic activity on the GMST is quantified using a 160,000 member ensemble that considers the uncertainty in the magnitude of aerosol radiative forcing and the strength of climate feedbacks. We find that in response to a rise in global radiative forcing of either 0.1 W m−2 or 0.15 W m−2 due to the reduction of sulfur emissions from international shipping, the associated rise in GMST by the end of 2024 is either 0.028 °C [0.025 to 0.031 °C, 5−95 % range] or 0.043 °C [0.038 to 0.046 °C], respectively. We also show that approximately 0.092 °C of the rise in annual mean GMST from 2022 to 2023 can be attributed to a shift from La Niña to El Niño conditions, which is approximately a third of the observed 0.3 °C rise in GMST between these two years. Additional increases in the annual mean GMST in 2023 and 2024 (both relative to 2022) of 0.075 °C [0.036 to 0.096 °C] and 0.053 °C [0.019 to 0.074 °C] are attributed, respectively, to a strong positive Indian Ocean Dipole (IOD) event that began in 2023. Our study is the first to suggest a significant contribution from the IOD to the anomalously high values of GMST observed in 2023 and 2024. Anomalously high Sea Surface Temperatures (SSTs) in the North Atlantic region led to a rise in GMST of 0.070 °C [0.054 to 0.094 °C] and 0.069 °C [0.055 to 0.091 °C] in 2023 and 2024 relative to 2022, respectively. This contribution is almost 90 % lower when the short-term variability component of North Atlantic SSTs is removed, resulting in lower estimates of the GMST anomaly in 2023 and 2024 than observed. These results suggest that short-term variability in the North Atlantic SSTs may have played a significant role in influencing the GMST anomalies in both 2023 and 2024; however, it is unclear whether this variability is internally or externally forced. Increased incoming solar radiation due to the 11-year solar cycle led to an additional rise in GMST of 0.025 °C [−0.009 to 0.051 °C] and 0.029 °C [−0.008 °C to 0.056 °C] in 2023 and 2024 relative to 2022, respectively. While the 2023 and 2024 GMST anomalies can be reconstructed fairly well from a combination of natural and anthropogenic factors, uncertainties remain in the reconstruction, driven primarily by the imprecise knowledge of the radiative forcing of aerosols, and the strength of climate feedbacks.
The manuscript presents a comprehensive analysis of the global mean surface anomalies observed in 2023 and 2024. It builds on previous studies that focussed on individual drivers of these anomalies by jointly estimating the contributions of anthropogenic activity, volcanic eruptions, 11-year cycle variability, El Nino Southern oscillation, Atlantic Multidecadal variability, Pacific Decadal Oscillation and Indian Ocean Dipole, using a multi linear regression energy balance model.
My only concern relates to section 2.1 and the Appendix, which would benefit from improvements in the presentation order and reproducibility of the results, as suggested in the specific comments. After these corrections, the manuscript is suitable for publication.
Specific Comments:
87-88: the paragraph starts explaining the first term, then the time grid and then the other terms of equation 1. This sentence applies to all terms of the equation and breaks the flow of the description of each term of the equation. I would first mention the time grid and then each term.
89: equation 2 is briefly mentioned here before the explanation of equation 1 is complete.
113-115: iIt is unclear whether AAWR represents the slope of dTanth that is obtained from a linear fit to temperature anomalies shown in panel 1a or the slope is obtained from a linear fit to dTanth. In the latter case, how is dTanth retrieved? Please rephrase the sentence.
119-120: The text says to “see caption” of figure 1, while the caption of Figure 1 similarly and repeatedly refers the reader back to the text (“see text”). In neither case is it clear what specific information the reader is expected or how it provides additional clarification. Please make these references explicit in the text and summarize the relevant information clearly in the caption of figure 1. Figure 1 contains a large amount of detail, and repeatedly moving back and forth between the text and the figure makes it difficult to follow the main point.
In these same lines: 1) it is unclear whether the term “single fit” refers to all panels in Figure 1 or only to panel a; 2) include the values of λ here as well, as is done in panel a, and refer to the section where you explained how you got this value; 3) does “the single time series of ERFaer” refer specifically to panel (b)?
134: While EBM acronym is introduced at line 60, the meaning of EBM-1 is not defined. It is only explained later in the Appendix (but not even at the beginning of the Appendix) and it is not straightforward what the number 1 means. A brief explanation should be provided here.
135: It only explains the temperature of the upper layer. What about the lower layer? You should also mention here how this approach is an improvement? I think that lines 591-600 belong here.
279-280: same as comment for lines 113-115.
284-285: This sentence is difficult to follow and would benefit from being rephrased.
286-288: Panel a should either be discussed first or moved to the last position in the figure. The discussion of Figure 2 begins at line 281 with panels b and c, which made me wonder whether I had missed something, as panel a is only discussed later.
303: EffCS is briefly mentioned here and in the introduction at lines 61-, where it is stated that “Our model [...] provides an estimate of Effective Climate Sensitivity (EffCS)”. The purpose of this sentence (L303) is unclear in its current form. The sentence should be expanded to provide substantive information about the EffCS estimate, as suggested in the Introduction, or it should be removed from this section.
326-328: It is unclear how the colors should be interpreted or how the probabilities were calculated. Additionally, the choice of colormap is confusing: the reds and blues appear to represent higher and lower probabilities, respectively, while white seems to indicate the most probable value, but it is actually the opposite.
394-400: can you provide a value for the respective change in TSI that corresponds to the change in GMST?
519-525: This paragraph discusses the anthropogenic warming rate, which was already covered in Section 3.2. Its placement in the section on the Indian Ocean Dipole feels abrupt; it would be more appropriately included in the discussion in Section 3.2.
Conclusion: The conclusion presented here is weaker than the one in Section 3.4 (lines 523–525). Consider expanding this section by incorporating the points made in those lines.
Appendix: Please provide the values or a range of values that have been used throughout the appendix to allow reproducibility of the results (gamma L 655; value used in equation A8, only few values are defined at L675-677; initial value of gamma used in L705, three values of Cu and Cd at L726-729).
Supplementary, L51:Define the latitudinal and longitudinal boundaries of the four regions to ensure data reproducibility
Technical corrections:
118: remove the brackets: (gamma, Geoffroy at al., 2013)