| 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.
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)”?