the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Oct 2025
Changes in 1958–2019 Greenland Surface Mass Balance are Attributable to both Greenhouse Gases and Anthropogenic Aerosols
Abstract. Greenland Ice Sheet (GrIS) mass loss is a main contributor to rising Global Mean Sea Level (GMSL), exhibiting decadal variability due to surface mass balance (SMB) changes. Greenhouse gases (GHG) have long been identified as a key driver of GrIS mass loss through warming-induced runoff. However, there has not been a formal attribution of historical GrIS SMB changes to GHG and the potential role for other forcings such as anthropogenic aerosols (AAER). Here, we use the Community Earth System Model version 2 large ensemble and single forcing large ensemble (CESM2-LE and CESM2-SFLE) to formulate a detection and attribution analysis for historical GrIS SMB changes. We show that the decadal variability of SMB is forced by historical radiative forcing attributable to both GHG and AAER through their forced changes of runoff. This highlights that, in addition to the frequently mentioned GHG, AAER also contributes to SMB changes during the historical period. GHG influences GrIS runoff mainly through long-term radiatively-forced warming, while AAER influences it through the decadal variability of atmospheric circulation that projects onto a Greenland blocking pattern, leading to relative cooling from cyclonic circulation over Greenland pre-1980 and relative warming from anti-cyclonic circulation thereafter. The attribution of SMB, and specifically runoff, to AAER has a lower signal-to-noise ratio (S/N) than the attribution to GHG due to both a weaker signal and wider confidence intervals. The lower S/N in attributing runoff changes to AAER is partly due to a smaller temperature response in AAER than in GHG and partly due to a mean state temperature dependency of the runoff sensitivity. In simulations with only AAER, the climate is colder than in simulations with all forcings or only GHG, leading to more time below freezing when temperature variations do not affect runoff as much. We resolve this issue by comparing simulations with all forcings with simulations in which everything-but-AAER is changing, thereby stressing the need to account for mean state dependencies when conducting detection and attribution with single forcing simulations.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-4249', Anonymous Referee #1, 11 Nov 2025
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AC1: 'Reply on RC1 - response to both reviewers (see supplement .pdf)', Yan-Ning Kuo, 23 Dec 2025
Dear Reviewer,
Thanks for the constructive feedback to improve the manuscript entitled “Changes in 1958-2019 Greenland Surface Mass Balance are Attributable to both Greenhouse Gases and Anthropogenic Aerosols” for the revision. Please find a point-by-point reply in the supplement .pdf, with both of reviewers' comments in black and our answers in blue.
Sincerely,
Yan-Ning Kuo, on behalf of the coauthors
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AC1: 'Reply on RC1 - response to both reviewers (see supplement .pdf)', Yan-Ning Kuo, 23 Dec 2025
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RC2: 'Comment on egusphere-2025-4249', Anonymous Referee #2, 19 Nov 2025
Review of manuscript “Changes in 1958–2019 Greenland Surface Mass Balance are Attributable to both Greenhouse Gases and Anthropogenic Aerosols”
By Kuo et al., 2025 - The Cryosphere
General comments
This study investigates the trend and variability of surface mass balance (SMB) and surface run-off over the Greenland icesheet (GrIS) and how these detectable signals can be attributed to anthropogenic aerosols (AAER) and Greenhouse Gases (GHG). Using a large ensemble of climate simulations from CESM2 with different sets of external forcings, combining with a Bayesian regression of fingerprints, the authors show that AAER contributes to the decadal variability of GrIS surface run-off and SMB through feedback to large-scale circulation pattern, i.e., strengthening Greenland blocking pattern, meanwhile GHG has an impact on long-term increase in surface air temperature. The study also addresses the issue of low signal-to-noise in attributing the impact of AAER due to the dependence on mean state temperature. This emphasises the need for improvement of method used in detection and attribution of climate change.
This study fits well to the scope of The Cryosphere, and to the best of my knowledge, is a missing piece in the literature. The manuscript is well-written with reasonable research questions and providing sufficient evidence to support the main findings. One major concern that should be addressed is to discuss further how AAER and GHG might have an impact on the NAO, the dominant of climate variability over the north Atlantic given that the authors refer to the NAO in the 3rd and 4th paragraph in the introduction and hypothesise that AAER contributes to GrIS SMB changes by modifying the circulation pattern projected into NAO. Although the manuscript discusses Greenland blocking, its relationship with the negative NAO in the summer, when SMB decrease happens, is less strong than in the winter. In addition, the positive NAO might contribute (equally to the AMOC) to the cold blob over the sub-polar gyre (Fan et al., 2023). Could this suggest a decrease in negative NAO (and hence Greenland blocking) in the GHG simulations? When this issue is addressed, I would be happy to accept this manuscript to be published in The Cryosphere.
Specific comments
Section 2.2.1: Did the author normalise both observed variable and fingerprints by internal variability (e.g., estimated from control runs) beforehand? Should this help with maximising signal-to-noise ratio?
L238: If I understand correctly, xAAER is equivalent to GHG forcing plus natural variability, and ALL-minus-xAAER is equivalent to AAER forcing plus nonlinear response, right?
Figure 4: The SMB sensitivity to TAS from AAER is close to zero, while it is significantly positive in ALL-minus-xAAER simulation (opposite to RACMO-ERA). However, the sensitivity of run-off to TAS in this ALL-minus-xAAER is close to RACMO-ERA, while the sensitivity in AAER is much lower than RACMO-ERA. Could another unknown factor play a role here?
L242-243: swapping order of AAER and GHG.
L255: GBI is mentioned for the first time, so it should be defined here. Besides, the time series of Greenland blocking indices were analysed in recent studies (e.g., Maddison et al., 2024; Luu et al., 2024) which suggested a sharp increase and well above zero since 2000 in observations. This seems different in Figure S6a which shows negative GBI values in a couple of years after 2000. Did the authors compute GBI from ERA5 or from RACMO-ERA?
L277-279: Perhaps it’s worth to compare with findings from Maddison et al. (2024) which also suggested AAER simulations (larger set of CMIP6) show some predictable signals of observed GBI variability, but the signals of the response to AAER is too weak in those simulations.
Section 4: I suggest adding a few sentences discussing the caveat of using only one GCM (CESM2) and the uncertainty of RACMO-ERA, although there are no other choices for observations of SMB.
Fan, Y., Liu, W., Zhang, P., Chen, R., and Li, L.: North Atlantic Oscillation contributes to the subpolar North Atlantic cooling in the past century, Climate Dynamics, 61, 5199-5215, 10.1007/s00382-023-06847-y, 2023.
Luu, L. N., Hanna, E., de Alwis Pitts, D., Maddison, J., Screen, J. A., Catto, J. L., and Fettweis, X.: Greenland summer blocking characteristics: an evaluation of a high-resolution multi-model ensemble, Climate Dynamics, 10.1007/s00382-024-07453-2, 2024.
Maddison, J. W., Catto, J. L., Hanna, E., Luu, L. N., and Screen, J. A.: Missing decadal variability of summer Greenland blocking in climate models, Submitted to Geophysical Research Letters, 2024.
Citation: https://doi.org/10.5194/egusphere-2025-4249-RC2 -
AC2: 'Reply on RC2 - response to both reviewers (see supplement .pdf)', Yan-Ning Kuo, 23 Dec 2025
Dear Reviewer,
Thanks for the constructive feedback to improve the manuscript entitled “Changes in 1958-2019 Greenland Surface Mass Balance are Attributable to both Greenhouse Gases and Anthropogenic Aerosols” for the revision. Please find a point-by-point reply in the supplement .pdf, with both of reviewers' comments in black and our answers in blue.
Sincerely,
Yan-Ning Kuo, on behalf of the coauthors
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AC2: 'Reply on RC2 - response to both reviewers (see supplement .pdf)', Yan-Ning Kuo, 23 Dec 2025
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Title: Changes in 1958–2019 Greenland Surface Mass Balance are Attributable to both Greenhouse Gases and Anthropogenic Aerosols
Authors: Kuo et al. (2025)
Journal: The Cryosphere
General comments
The manuscript presents a rigorous detection and attribution analysis on historical Greenland Ice Sheet (GrIS) surface mass balance (SMB) changes, using a Bayesian total least square (TLS) regression framework that explicitly accounts for multiple sources of uncertainty. Using CESM2 large ensemble and single forcing large ensemble, with regional climate model RACMO outputs as a reference, the authors show that historical GrIS SMB changes can be attributed not only to greenhouse gases (GHG) but also to anthropogenic aerosols (AAER) through their forced changes on runoff. The study finds that GHG primarily drive the long-term trend, whereas AAER contribute through decadal atmospheric circulation variability, particularly a Greenland blocking pattern. The authors further explain the lower signal-to-noise ratio associated with AAER attribution and address the temperature state dependence of such attribution, highlighting the need for future methodological improvements.
Overall, the manuscript is very well written, clearly structured, and the results are well presented with helpful supportive information. The Bayesian TLS regression approach provides a robust quantification of regression uncertainties, addressing a key challenge in detection and attribution studies of ice sheet changes given the limited data. The work also demonstrates that parts of the historical GrIS SMB change are attributable to AAER for the first time. I find the work novel and inspirational, and I expect the results will be of broad interest to the community. One thing related to the key conclusion needs to be justified further is whether GHG forcing also contributes to the decadal variability of GrIS SMB and runoff changes. Additional comments can be found below to improve clarity and strengthen the discussion. Once these issues are addressed, I would be very happy to support prompt publication of this paper in The Cryosphere.
Specific comments
Line 30-31: in addition to calving, ice discharge can also come from oceanic melting
Line 36: “surface melting” would be more accurate than “ice melting”
Line 103: Can add a citation for the statement “as GHG and AAER are two dominant anthropogenic forcings for historical climate change.”
Line 182: It seems that the Frederikse et al. (2020) reconstruction does include RACMO SMB data in its input-output estimate (Mouginot et al., 2019). Therefore, it will be more accurate to just say something like “by the fact that the reconstructed GrIS mass loss includes RACMO-simulated SMB.”
Figure 2, 3: Maybe it can add more clarity to restate the y (RACMO-ERA) and x (ensemble mean of CESM2) for regression in the captions.
Figure S4: It seems that xAAER has larger increase in runoff or decrease in SMB than GHG. What do you think could be the possible reason, e.g., related to the temperature state dependence?
Line 225: Maybe add something like “usually” to the statement “βAAER > 1 and also > βGHG”
Section 3.3 and Figure 4: The same Bayesian TLS regression is applied to estimate the temperature sensitivity of SMB and R to TAS. Although it is pointed to Table S1 in Section 2.2.1, it will add more clarity by stating what the y and x are for the regression, either in the figure caption or in the text. Does each regression use the annual SMB and TAS from the corresponding simulation? Does the sensitivity (Gt per year per 1K warming) equal to the scaling factor?
Figure S6: in panel (a), is the GBI time series in black calculated from RACMO output or directly from ERA5?
Section 3.4 and Figure 5: It is well illustrated that there is an AAER-forced change in the variability of circulation, imprinted onto a pattern that reinforces Greenland blocking, by comparing the correlation patterns in AAER and ERA5 (Fig.5f,e). However, another question remains that if GHG also contribute to circulation variability in addition to the long-term linear trend. Thus, I am curious what the correlation map for GHG would look like (e.g., whether it will have a similar pattern as panel (e) and (f)).
Discussion: it can benefit from adding more discussion about the structural model uncertainty (of using one climate model CESM2 and one regional climate model RACMO).
Technical corrections
Line 84: “ran” to “run”
Line 181: “by the GrIS”
Figure 1: 2nd line in caption: maybe rephrase as “The anomalous (long-term mean subtracted) annual GrIS mass loss from the Frederikse et al., (2020) reconstruction”
Figure 4: 3rd line in caption: the annotation “(ALL, GHG, AAER; purple, red, blue, light blue respectively)” needs to be completed or can be removed since it is already stated for panel (a)
Line 243: reverse the order of “AAER and GHG”
Line 252: This sentence “to explain the GrIS runoff changes, which are linked to the melting-induced runoff changes” seems repetitive.
Figure 5: Maybe remove “GrIS” in the titles of panel (a)-(d)
Line 262: Consider adding “trend” after “All-forced Z500”
Line 304: “than” to “that”
Supplementary Information:
S1: first line: add space to “implement a Markov Chain…”
S1: 13th line: “(green line in Figure S1a)”?