the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Jun 2025
The ENSO-driven bias in the assessment of long-term cloud feedback to global warming
Abstract. Accurately assessing the cloud feedback to global warming is essential for producing reliable climate projections. Linear regression analysis is a widely used method for this purpose, offering a straightforward approach for examining the relationship between cloud radiative effects and global mean surface temperature. However, the El Niño–Southern Oscillation (ENSO) can introduce a significant bias in these estimations, which is often overlooked due to ENSO’s relatively short periodicity. Using 72 years of reanalysis data and 150 years of simulations by 12 global climate models, this study demonstrates that ENSO can produce a bias of comparable magnitude to the estimated cloud feedback, over decades and even centuries. By providing a detailed spatial and temporal analysis of this bias, our findings underscore the importance of accounting for and removing the ENSO’s influence to improve the accuracy of cloud feedback assessment in the context of global warming.
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Status: open (until 25 Jul 2025)
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RC1: 'Comment on egusphere-2025-2574', Anonymous Referee #1, 01 Jul 2025
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This study investigates the influence of ENSO on cloud feedback estimates under global warming, using a regression-based de-ENSO method. Based on reanalysis observations and GCM simulations, the authors find that ENSO variability biases estimates of long-term cloud feedback to global warming both in the historical record and the abrupt4xCO2 experiment, with large impacts on regional scales.
Overall, the paper addresses an interesting topic. However, I have several major concerns regarding the methodology and the main findings. Some of them may arise from a lack of clarity in the Method section. I strongly suggest that the authors improve the clarity of the manuscript, particularly by providing a clearer and more detailed explanation of the main framework.
Major comments on the methodology:
1. My main question regarding the method: Is the ONI (ENSO timeseries) detrended? This is critical because the proposed linear framework (Eq. 1)
Y = a x time + b x ONI + c,
aims to separate the long-term trends (the first term, a x time) from ENSO-related variability (the second term, b x ONI). If the ONI itself contains a long-term trend, this could lead to double-counting of changes in the targeted variable (CRE or GMST) that are associated with tropical mean-state SST changes. I’m wondering if this may be the case in the abrupt4xCO2 analysis (see more on this below). In its current form, the manuscript doesn’t clearly state whether the ONI has been detrended. The text mentions that the ONI was bandpass-filtered, but the filtering timescale was not specified (it only says “to remove ONI variations beyond ENSO’s typical periodicities”). Also, Line 106 “it retains the ENSO-induced long-term trend effect” seems to suggest the ONI trend is retained. Moreover, Lines 134-136, which discuss results from GCMs, also suggest that no detrending is applied to the ONI timeseries before the decomposition. In either case, more clarification is needed. If the ONI is indeed not detrended, I’m concerned about the linearity of this method and would appreciate the authors’ comments.
2. My second methodological concern is related to the residual term (c in Eq. 1): How large is its contribution? Is it sufficiently small that one can justify focusing only on the first two terms, as done in the paper? Figs. 2bc show that the sum of GMST variance explained by the first two terms is notably less than 1, and can be even smaller than 0.5 (depending on the period). This again raises questions about whether this linear decomposition is appropriate and whether the unexplained residual term undermines the interpretation of the results.
3. Finally, Figure 1 shows that ENSO’s influence on long-term changes decreases with time. Given this, it is unclear to me why the authors choose to focus on an arbitrary 40-yr period (1982-2021) throughout the paper, especially since the ERA5 reanalysis data used in this paper spans from 1950-2021.
Overall, I think the paper suffers from a lack of clarity in the Method section and would benefit from substantial revision and clarification.
Major comments on the Results:
1. CRE decomposition in Fig. 3
According to Eq. 1, the linear framework decomposes total CRE variations into two components: (1) a linear trend term (a x time) and (2) the portion associated with ENSO variability (b x ONI). In Fig. 3, however, term (1) is interpreted as the CRE change driven by the warming trend, which I find difficult to justify. It assumes that the trend in CRE is equivalent to the CRE response to long-term warming, which may not be valid. This issue may arise from ambiguity in Eq. 1. Specifically, what is the unit of the coefficient a? Is it the trend unit of the targeted variable (e.g. for CRE, it would be W/m2/year), or is it a regression coefficient with respect to long-term global warming (W/m2/K)? If it’s the former (seems more likely based on Eq. 1), I do not think it can be interpreted as “CER due to warming trend”. Either way, this concern highlights a fundamental confusion in the framework that needs to be clarified.
2. ENSO-related biases in 4xCO2 experiment
I was quite surprised by the (really) large ENSO-related biases in cloud feedback estimates from the 150-yr abrupt 4xCO2 simulations (Figs. 6, 7), considering (1) the long timescales (150-yr) of the experiment and (2) the potential high signal-to-noise ratio in this strong forcing scenario. It again raises concerns related to the methodological question of whether the ONI has been detrended. If the ONI timeseries contains a strong linear trend in this case, the trend could actually reflect forced mean-state changes rather than ENSO variability. In that case, the current method might be attributing part of the long-term signal to ENSO, thus overestimating the ENSO-related contributions by double-counting tropical Pacific SST trends.
To address this, I suggest the authors show the timeseries of GSMT and global-mean CRE over the course of the simulations, either for each model or one representative model. It should include both the full variations as well as their decomposed components (the long-term trends and ENSO-related variations). This would allow us to directly asses the evolution and relative magnitude of the two terms, and to verify whether the ENSO related signals are not being mixed with the global warming trend.
Specific comments
- Figure 1: The filtered ONI timeseries appears to have removed much of the high-frequency variability rather than the low-frequency variability?
- Line 85: Why not use SST (a readily available variable in GCM output) to compute the ONI to be consistent with observations?
- Line 105-106: what “delayed components of ENSO-related variations” are referred to here?
- Line 135: Does this mean that 9 out of 12 GCMs actually show a significant ENSO/ONI trend over this period? If so, does this linear decomposition still hold?
- Line 143-144: This sentence is very confusing and unclear, I do not understand what is meant here. Please consider rephrasing.
- Line 149: Based on Fig. 2b, the variance in GSMT explained by the trend over a randomly-selected 40yr period (other than 1982-2021) can be as low as 0.3. Similarly, ENSO’s contribution (R2_ONI) is up to 0.1. This suggests that more than 50% of the total GMST variance could come from the residual term. If so, this linear decomposition doesn’t seem to work well and may not accurately reproduce the original variance.
- Line 165: What exactly is meant by “covariations between clouds and the warming trend”? As noted in my major concerns, is this essentially just the trend in CRE?
- Line 169: While it’s true that ENSO has a relatively small impact on the GMST during this period, it may have a more notable impact on regional surface temperature variations (e.g. in the tropical Pacific). If so, this statement may be unfair. For a more solid comparison, I suggest the authors show spatial maps of surface temperature variance explained by the warming trend and by ENSO (similar to Fig. 3 but for TS instead of CRE). In addition, it would also be helpful to show the global-mean CRE timeseries along with its decompositions into the linear trend and ENSO-related component (similar to Fig. 1 but for global-mean CRE instead of GMST).
- Related to Fig. 3: how much of the regional variance is associated with the residual term? It would be informative to provide another row of panels showing the contribution of the residual term (c in Eq. 1)
Citation: https://doi.org/10.5194/egusphere-2025-2574-RC1
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