the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jul 2025
Emergence of climate change signal in CMIP6 extreme indices
Abstract. Climate and weather extremes are becoming more and more frequent due to the influence of anthropogenic climate change and knowing when and where we can expect these effects to occur is essential for both climate change mitigation and developing adaptation measures. We investigate the time of emergence – meaning the earliest time at which the climate change signal can be detected from the noise of natural variability – for 27 climate extreme indices related to surface temperature and precipitation. An ensemble of 21 CMIP6 global climate models (including several with a large number of initializations) is combined with a model weighting scheme that accounts for both model performance and independence to provide robust ensemble statistics of the emergence of climate extremes and to explore model uncertainty.
The results from this comprehensive study indicate that spatial and temporal emergence patterns differ between temperature indices related to absolute values and percentiles, annual maxima and minima indices, and also between seasons for individual indices. Precipitation indices tend to emerge much later and mostly only under high emissions scenarios. The main regions where precipitation emergence occurs are the northern high latitudes and central Africa, although between-model variability is often quite high.
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RC1: 'Comment on egusphere-2025-3331', Anonymous Referee #1, 25 Jul 2025
Review of “Emergence of climate change signal in CMIP6 extreme indices” by Schuhen et al.
This study explores emergence of climate extreme indices in the CMIP6 ensemble. A robust early detection of temperature extremes, especially at low latitudes, is found. Also, some emergence of increasing precipitation extremes in northern high latitude regions is found.
Overall, this is a comprehensive study that is well written. My comments are mostly minor, however, I do raise a few methodological queries regarding the KS test and detection of changes in bounded/non-gaussian distributions (see specific comments below). I also think the novelty of this work isn’t as clear as it could be and some edits to address this would help readers.
Specific comments:
Abstract: The first sentence describes extremes as “effects” which sounds strange. The last sentence uses the term “often quite high” which is vague. In general, while the abstract is short already the text that is already there could be more concise (e.g. removing “more and” in the first sentence) and additional space could be used to provide more insight into the results.
Introduction: This is well written but is a bit too brief and doesn’t specifically discuss what has been found in previous analyses on emergence of extremes (e.g. Bador et al., 2016; Harrington et al., 2016; King et al., 2015) and what this specific study adds to the literature. I note these references are mostly referred to later in the paper, although Bador et al. is hopefully a useful addition. On face value the main novel aspects of this work are its comprehensive nature (examining all ETCCDI indices), its use of CMIP6 models, and the model weighting approach, but I’m wondering if I’m missing something else. There’s also been work to explore emergence of fire weather metrics which may be worth discussing (Abatzoglou et al., 2019).
L65-71: I think some discussion of the fact that KS tests for emergence estimation have been employed previously would be worthwhile including (King et al., 2015; Mahlstein et al., 2011, 2012). There are other methods that could be applied to extreme indices, such as signal-to-noise ratios (Hawkins et al., 2020) or probability ratios (Harrington et al., 2016; King et al., 2016), so some discussion of why those methods aren’t suitable are preferable here might be of use.
L69-71: Could you clarify if the autocorrelation test is performed on the raw index values or if a detrending is performed first? I suspect it wouldn’t make a large difference but it would be worth clarifying.
L82: Perhaps change “immense” to “high”.
L97: The broadening of this distribution is interesting and I suppose could be to do with a higher rate of warming or increasing variability or a combination of the two. This is probably beyond the scope of your study to explore.
L108: Too many parentheses on the reference.
Section 2.2.2: If I understand correctly, the same model weighting is applied at each gridcell- this could be a bit clearer perhaps. I suppose this might mean a regional ToE analysis would involve apply a different model weighting and potentially have different results. I think this would be worth mentioning as a minor caveat.
Results: This is a very clear and well written section. I might have missed something with the TN10P calculation but I don’t understand how results below -10% are possible (as shown in Figure 6a-c) given it is an index that is bounded and starts off with a climatological value of 10% and a possible minimum of 0%. More generally, I would wonder about KS test performance with the TX/N90/10P indices given the bounds and the fact that the KS test is sensitive to changes in the width of the distributions. This might be worth commenting on especially given earlier emergence relative to the absolute indices.
L184: “patters” should be “patterns”.
Seasonal results: It might be worth noting that the impacts/relevance of different indices may differ when performing seasonal analysis, e.g. JJA TNn ToE in Northern Hemisphere locations may be of less relevance to impacts than TXx or TX90P.
L310: I wonder if this finding is also related to the skewed nature of Rx1day and the lower bound of 0. Perhaps this makes detection of declines harder.
Summary: This section is also well written but a little brief. Some discussion of consistencies with other ToE studies, including analyses of mean changes, may be helpful, as well as clearer elucidation of what has been learnt from this paper specifically.
References
Abatzoglou, J. T., Williams, A. P., & Barbero, R. (2019). Global Emergence of Anthropogenic Climate Change in Fire Weather Indices. Geophysical Research Letters, 46(1), 326–336. https://doi.org/10.1029/2018GL080959
Bador, M., Terray, L., & Boé, J. (2016). Emergence of human influence on summer record-breaking temperatures over Europe. Geophysical Research Letters, 43(1), 404–412. https://doi.org/10.1002/2015GL066560
Harrington, L. J., Frame, D. J., Fischer, E. M., Hawkins, E., Joshi, M. M., & Jones, C. D. (2016). Poorest countries experience earlier anthropogenic emergence of daily temperature extremes. Environmental Research Letters, 11(5), 055007. https://doi.org/10.1088/1748-9326/11/5/055007
Hawkins, E., Frame, D. J., Harrington, L., Joshi, M. M., King, A. D., Rojas, M., & Sutton, R. (2020). Observed Emergence of the Climate Change Signal: From the Familiar to the Unknown. Geophysical Research Letters, 47(6). https://doi.org/10.1029/2019GL086259
King, A. D., Black, M. T., Min, S.-K., Fischer, E. M., Mitchell, D. M., Harrington, L. J., & Perkins‐Kirkpatrick, S. E. (2016). Emergence of heat extremes attributable to anthropogenic influences. Geophysical Research Letters, 43(7), 3438–3443. https://doi.org/10.1002/2015GL067448
King, A. D., Donat, M. G., Fischer, E. M., Hawkins, E., Alexander, L. V., Karoly, D. J., Dittus, A. J., Lewis, S. C., & Perkins, S. E. (2015). The timing of anthropogenic emergence in simulated climate extremes. Environmental Research Letters, 10(9), 094015. https://doi.org/10.1088/1748-9326/10/9/094015
Mahlstein, I., Hegerl, G., & Solomon, S. (2012). Emerging local warming signals in observational data. Geophysical Research Letters, 39(21), n/a-n/a. https://doi.org/10.1029/2012GL053952
Mahlstein, I., Knutti, R., Solomon, S., & Portmann, R. W. (2011). Early onset of significant local warming in low latitude countries. Environmental Research Letters, 6(3), 034009. https://doi.org/10.1088/1748-9326/6/3/034009
Citation: https://doi.org/10.5194/egusphere-2025-3331-RC1 -
RC2: 'Comment on egusphere-2025-3331', Anonymous Referee #2, 14 Aug 2025
The authors present future changes of extreme climate indices at the end of the 21. century 2070-2100 and they present the 20 year time slice, when these changes are starting to be robust [ToE]. But they do not make a connection between the two, which would be interesting and new.
The paper is very descriptive and does not give any physical explanation, why regional differences occur. An explanation would be helpful as well as an example for an impact on society which is mentioned in the introduction. The results are sufficiently presented, only interpretations and conclusions of the results are sparse in the paper.
Overall it is well written, but in some paragraphs additional text and explanation would be helpful for the reader.
specific comments
Chapter 2.2.2: Why not explore the new ensemble from Merrifield, A. L., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications, Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, 2023.
The paper is missing connections to the previous studies concerning the results of extreme climate extreme indices of the CMIP6 ensemble. In general there are only a few references. Extreme climate indices based on the CMIP6 model ensemble have been publish already as well as the robustness of these changes for different time slices and different warming levels. (e.g. IPCC Atlas, Schwingshackl, C., Sillmann, J., Vicedo-Cabrera, A. M., Sandstad, M., & Aunan, K. (2021). Heat stress indicators in CMIP6: Estimating future trends and exceedances of impact-relevant thresholds. Earth's Future, 9, e2020EF001885. https://doi.org/10.1029/2020EF001885, Coppola, E., Raffaele, F., Giorgi, F. et al. Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble. Clim Dyn 57, 1293–1383 (2021). https://doi.org/10.1007/s00382-021-05640-z)
Line 132: To expand the results of the research, it would be interesting to also use SSP3.7 and those have not been published as much.
DTR , it could be helpful for the interpretation to look at it seasonally.
Precipitation: you can have a positive or negative change, how do you combine this with the ensemble results of ToE?
Concerning the change of precipitation extreme indices it could be helpful to additionally look at the monsoon season in the areas experiencing monsoon.
Technical corrections:
The figures are too small, add median to the subtitle and longitude/latitude
L 11-12: what do you mean by ‘although between-model variability is often quite high’ ? Could you add a little more information?
L 30-33: Start the sentence with precipitation to make it more clear.
L 90: add ‘computer’ to power limitations
L 138: you explain the hatching, but it is not clearly written, use the text from figure 3.
L 143/176/196/ ....., do not use trend (this is also a statistical measure), better increase
L 145: add median
L 147: this still belongs to figure 3, why start a new paragraph, or add a break in line 140 too.
Caption of figure 3: Replace : ‘available’ with selected
L 152: you talk about the Northern Central America region , it would be better to say that this is IPCC AR5 reference region and perhaps add the figure from, https://github.com/IPCC-WG1/Atlas?tab=readme-ov-file#new-reference-regions, than it is clear and not clear for the rest of the text e.g. line 256 I miss understood at the first time.
L 153: add paragraph
L 186: I think something is missing in the sentence before the comma.
L 188: I do not agree, only the distributions differ earlier, but you are comparing two different indices
L 219: delete ‘strong’ it is not correct
L 214: what do you mean by ‘but future changes could be even more drastic’,
L 220: perhaps rising instead of ‘warming’
L 226: ‘strong’: I do not agree
L 228: Sahara and south of it
L 235: What do you mean by ‘but sign o model disagreement ....’, please rephrase
L 297: I think introducing CMIP5 is not helpful, you have a lot of results already and CMIP5 and your specific ensemble are not comparable. You could e.g. mention something like ‘our findings are in line with previous studies by Ling et al. 2015’.
L 305: experience an decrease instead of ‘see a negative change’
L 306: delete ‘yet’
L 310: ’seem to translate into emergence’, please revise
L 333: delete ‘trends’
L 340: This does not make sense or I did not understand the method, this means that non of the 20 year time slice have the same distribution like 1850-1900. But I think this should be the case in 1850-1900. I did not see the code, how you calculated the TN10p I only found the download link. But it could be possible, that your sample size is too small for such kind of analysis.
Citation: https://doi.org/10.5194/egusphere-2025-3331-RC2
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