Multi-decadal initialized climate predictions using the EC-Earth3 global climate model

Mahmood, Rashed; Donat, Markus G.; Bilbao, Roberto; Ortega, Pablo; Lapin, Vladimir; Tourigny, Etienne; Doblas-Reyes, Francisco

doi:https://doi.org/10.5194/egusphere-2025-1208

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https://doi.org/10.5194/egusphere-2025-1208

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27 Mar 2025

| 27 Mar 2025

Multi-decadal initialized climate predictions using the EC-Earth3 global climate model

Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes

Abstract. Initialized climate predictions are routinely carried out at many global institutions that predict the climate up to next ten years. In this study we present 30 year long initialized climate predictions and hindcasts consisting of 10 ensemble members. We assess the skill of the predictions of surface air temperature on decadal and multidecadal timescales. For the 10 year average hindcasts, we find that there is limited added value from initialization beyond the first decade over a few regions. However, no added value from initialization was found for the third decade (i.e. forecast years 21–29). The ensemble spread in the initialized predictions grows larger with the forecast time, however, the initialized predictions do not necessarily converge towards the uninitialized climate projections within a few years and even decades after initialization. There is in particular a long-term weakening of the Atlantic Meridional Overturning Circulation (AMOC) after initialization that does not recover within the 30 years of the simulations, remaining substantially lower compared to the AMOC in the uninitialized historical simulations. The lower AMOC mean conditions also result in different surface temperature anomalies over northern and southern high latitude regions with cooler temperature in the northern hemisphere and warmer in the southern hemisphere in the later forecast years as compared to the first forecast year. The temperature differences are due to less transport of heat to the northern hemisphere in the later forecast years. These multi-decadal predictions therefore highlight important issues with current prediction systems, resulting in long-term drift into climate states inconsistent with the climate simulated by the historical simulations.

Received: 14 Mar 2025 – Discussion started: 27 Mar 2025

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30 Oct 2025

Multi-decadal initialized climate predictions using the EC-Earth3 global climate model

Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes

Earth Syst. Dynam., 16, 1923–1934, https://doi.org/10.5194/esd-16-1923-2025,https://doi.org/10.5194/esd-16-1923-2025, 2025

Short summary

Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-1208', Anonymous Referee #1, 21 Apr 2025
Summary
The study by Mahmood et al. makes an important step forward in understanding initialized prediction on decadal timescales (out to 30 years), a topic that is only rarely addressed due to computation costs. The results are very interesting, and ultimately seem to hinge on the fact that the initialized runs push AMOC into a weakened state that is outside the range of what is captured by the uninitialized simulations. The authors could enhance the impact of this study by focusing in a bit more on this topic – is this a realistic plausible reality? Why do these differences exist in the Lab Sea convection? And if initialized predictions are perhaps unreliable at this timescale (multi-decadal), should the community be pushing instead towards what the authors refer to as “variability-constrained projections” (which would benefit from even a brief description).

Major Comments:
The discussion in the first paragraph of Section 3.1 should much more nuanced. The authors state that most land areas show ACC that is positive and statistically significant, with an exception of South America; But looking at Fig 1, there is a lot of stippling indicating things are not significant, even over land. Signals over Australia, for example, are rarely significant. In comparing with Fig S1, again it seems that significance has actually dropped dramatically in the case with fewer years (most notable over the Indian Ocean and Pacific). This calls into question the assertion that “a reduced sample size (due to initializing by every fifth year) does not strongly affect the skill of the prediction system.”

Figure 1: Given how much of the signal is not significant and thus stippled, I wonder if the fig would be more readable if stippling indicated where things are significant? It’s hard to discern at first glance right now.

Discussion of Fig 2 notes the different mean state in initialized predictions, but should also discuss the much larger bias as a result.

The discussion around the Labrador Sea is interesting, as it points to inherent differences between initialized and uninitialized models. Some added context would strengthen the section further; for instance, is a shutdown in the Lab Sea convection rooted in any observations? Is it realistic, or an unwanted model behavior? Why does this happen in initialized runs?

Line 305: “The climate predictions based on these variability-constrained projections (e.g. Mahmood et al., 2022; Donat et al., 2024) even show higher and more widespread added skill, compared to the initialized predictions presented in this study, for both 10 and 20 year mean predictions.” – this seems like a huge point, but isn’t backed up by comparison within this text. It would make a stronger paper overall to focus in on this as a piece of the larger manuscript.

Minor Comments
Line 51: What is meant by “constraining patterns of climate variability in large ensembles of the uninitialized projection simulations”? Some added description would help.

Line 79: “…initialized on the first of November every 5^th year…” – is there any rationale for using November? Is this consistent with other experiments? Would differences be expected if initializing in spring/summer instead of fall/winter? (Perhaps the Lab Sea response would differ in particular?)

Line 90: “…on the order of 10-5 K)” – to clarify, is this meant to be 1e-5? That seems like it might actually be rather large, but I’m typically working in uninitialized space, wherein 1e-14 perturbations are common. Could the authors comment on if this is a standard magnitude for initialized predictions?

Line 106: “…to a uniform five-degree grid” – that’s really coarse; is there a reason to convert to five degrees? Is this the native resolution of some runs vs others?

Line 141: “…evaluation periods are different (i.e. 1961-2020 for FY1-10, 1971-2020 for FY11-20 and 1971-2020 for FY21-29).” – Could you limit the evaluation period for FY1-10 to 1971 onwards arbitrarily, just to test the impact?

Line 149: “…over land regions including northwest Canada and USA.” – I’m not sure I see a lot of added value over the USA, it’s mostly stippled? But Australia and the Middle East seem to show something more cohesive.

Line 162: “Previous studies have shown that the decadal predictions can be highly skillful in forecasting temperature over SPNA region.” – Citations?

Fig 2: Please add a legend for the lines, in addition to the description the caption for easier readability.

Fig 5: Suggest adding stippling for significance, as in other maps
Citation: https://doi.org/10.5194/egusphere-2025-1208-RC1
- AC1: 'Reply on RC1', Rashed Mahmood, 08 Sep 2025
  
  Please see the attached file where we provide responses to all referee comments.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1208-AC1
RC2:
'Comment on egusphere-2025-1208', Anonymous Referee #2, 08 Aug 2025

The added value of initialization for long (multidecadal) climate outlooks remains unclear, so this analysis of 30-year initialized predictions using the EC-Earth3 model will be of considerable interest to the decadal prediction community. The manuscript is clear and concise, and the results are intriguing. I recommend this be published after some minor issues are addressed. Most importantly, I think an additional analysis is needed (perhaps in supplemental) that shows how skill varies with lead time when a common verification window is used.

Minor Comments
L48-60: A recent study suggests that initialized internal variability could constrain surface climate variability for multiple decades (Deser et al., 2025, https://doi.org/10.1007/s00382-024-07553-z). It may be worth including such potential predictability examples in this intro section.
L62: Unclear what “model dependence in previous results” refers to exactly
L112: If hindcasts are 30 years long, why not examine FY21-30 and FY11-30, for consistency?
L121: Should mention that observed anomalies are computed in similar fashion (observed climatology matching months/years used for hindcast climatology).
Fig. 1: Caption should state the field being analyzed. Methods should clarify how statistical significance is quantified.
L137: Authors have an opportunity here to directly address skill sensitivities to sampling and system design choices, so why not do that in Fig. S1? A first FY1-10 ACC plot could subsample the previous system, selecting only start dates used in Fig. 1 (this would isolate the effects of changes in initialization, I believe). A second FY1-10 ACC plot could show skill when all start dates are included from the previous system (which would reveal the effect of reduced hindcast sampling on skill).
L142: I think this should read “1981-2020 for FY21-29”? As noted above, the use of 9-year average (instead of 10-year average) for FY21-29 introduces another avoidable difference. The differences in verification window are likely important, so why not include a supplemental figure that shows skill for a common verification window (1981-2020 for all leads)? Otherwise, the conclusions that can be drawn from this comparison are very limited.
L152: I think the conclusions about impact of initialization are somewhat shaky given the discrepancies in verification window (see above).
L187: I would delete “as it was assumed in the past”, because I don’t consider fast convergence to be a generally held assumption, particularly for the North Atlantic.
L213-215: Repetitive with introduction.
Fig. 4: Is it surprising that the AMOC climatology in DP_30yrs is almost identical to that from the DP (10-year), even at short leads, despite the different initialization methods?

Citation: https://doi.org/10.5194/egusphere-2025-1208-RC2
- AC2: 'Reply on RC2', Rashed Mahmood, 08 Sep 2025
  
  Please see the attached pdf file where we provide author response to the referee comments.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1208-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-1208', Anonymous Referee #1, 21 Apr 2025
Summary
The study by Mahmood et al. makes an important step forward in understanding initialized prediction on decadal timescales (out to 30 years), a topic that is only rarely addressed due to computation costs. The results are very interesting, and ultimately seem to hinge on the fact that the initialized runs push AMOC into a weakened state that is outside the range of what is captured by the uninitialized simulations. The authors could enhance the impact of this study by focusing in a bit more on this topic – is this a realistic plausible reality? Why do these differences exist in the Lab Sea convection? And if initialized predictions are perhaps unreliable at this timescale (multi-decadal), should the community be pushing instead towards what the authors refer to as “variability-constrained projections” (which would benefit from even a brief description).

Major Comments:
The discussion in the first paragraph of Section 3.1 should much more nuanced. The authors state that most land areas show ACC that is positive and statistically significant, with an exception of South America; But looking at Fig 1, there is a lot of stippling indicating things are not significant, even over land. Signals over Australia, for example, are rarely significant. In comparing with Fig S1, again it seems that significance has actually dropped dramatically in the case with fewer years (most notable over the Indian Ocean and Pacific). This calls into question the assertion that “a reduced sample size (due to initializing by every fifth year) does not strongly affect the skill of the prediction system.”

Figure 1: Given how much of the signal is not significant and thus stippled, I wonder if the fig would be more readable if stippling indicated where things are significant? It’s hard to discern at first glance right now.

Discussion of Fig 2 notes the different mean state in initialized predictions, but should also discuss the much larger bias as a result.

The discussion around the Labrador Sea is interesting, as it points to inherent differences between initialized and uninitialized models. Some added context would strengthen the section further; for instance, is a shutdown in the Lab Sea convection rooted in any observations? Is it realistic, or an unwanted model behavior? Why does this happen in initialized runs?

Line 305: “The climate predictions based on these variability-constrained projections (e.g. Mahmood et al., 2022; Donat et al., 2024) even show higher and more widespread added skill, compared to the initialized predictions presented in this study, for both 10 and 20 year mean predictions.” – this seems like a huge point, but isn’t backed up by comparison within this text. It would make a stronger paper overall to focus in on this as a piece of the larger manuscript.

Minor Comments
Line 51: What is meant by “constraining patterns of climate variability in large ensembles of the uninitialized projection simulations”? Some added description would help.

Line 79: “…initialized on the first of November every 5^th year…” – is there any rationale for using November? Is this consistent with other experiments? Would differences be expected if initializing in spring/summer instead of fall/winter? (Perhaps the Lab Sea response would differ in particular?)

Line 90: “…on the order of 10-5 K)” – to clarify, is this meant to be 1e-5? That seems like it might actually be rather large, but I’m typically working in uninitialized space, wherein 1e-14 perturbations are common. Could the authors comment on if this is a standard magnitude for initialized predictions?

Line 106: “…to a uniform five-degree grid” – that’s really coarse; is there a reason to convert to five degrees? Is this the native resolution of some runs vs others?

Line 141: “…evaluation periods are different (i.e. 1961-2020 for FY1-10, 1971-2020 for FY11-20 and 1971-2020 for FY21-29).” – Could you limit the evaluation period for FY1-10 to 1971 onwards arbitrarily, just to test the impact?

Line 149: “…over land regions including northwest Canada and USA.” – I’m not sure I see a lot of added value over the USA, it’s mostly stippled? But Australia and the Middle East seem to show something more cohesive.

Line 162: “Previous studies have shown that the decadal predictions can be highly skillful in forecasting temperature over SPNA region.” – Citations?

Fig 2: Please add a legend for the lines, in addition to the description the caption for easier readability.

Fig 5: Suggest adding stippling for significance, as in other maps
Citation: https://doi.org/10.5194/egusphere-2025-1208-RC1
- AC1: 'Reply on RC1', Rashed Mahmood, 08 Sep 2025
  
  Please see the attached file where we provide responses to all referee comments.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1208-AC1
RC2:
'Comment on egusphere-2025-1208', Anonymous Referee #2, 08 Aug 2025

The added value of initialization for long (multidecadal) climate outlooks remains unclear, so this analysis of 30-year initialized predictions using the EC-Earth3 model will be of considerable interest to the decadal prediction community. The manuscript is clear and concise, and the results are intriguing. I recommend this be published after some minor issues are addressed. Most importantly, I think an additional analysis is needed (perhaps in supplemental) that shows how skill varies with lead time when a common verification window is used.

Minor Comments
L48-60: A recent study suggests that initialized internal variability could constrain surface climate variability for multiple decades (Deser et al., 2025, https://doi.org/10.1007/s00382-024-07553-z). It may be worth including such potential predictability examples in this intro section.
L62: Unclear what “model dependence in previous results” refers to exactly
L112: If hindcasts are 30 years long, why not examine FY21-30 and FY11-30, for consistency?
L121: Should mention that observed anomalies are computed in similar fashion (observed climatology matching months/years used for hindcast climatology).
Fig. 1: Caption should state the field being analyzed. Methods should clarify how statistical significance is quantified.
L137: Authors have an opportunity here to directly address skill sensitivities to sampling and system design choices, so why not do that in Fig. S1? A first FY1-10 ACC plot could subsample the previous system, selecting only start dates used in Fig. 1 (this would isolate the effects of changes in initialization, I believe). A second FY1-10 ACC plot could show skill when all start dates are included from the previous system (which would reveal the effect of reduced hindcast sampling on skill).
L142: I think this should read “1981-2020 for FY21-29”? As noted above, the use of 9-year average (instead of 10-year average) for FY21-29 introduces another avoidable difference. The differences in verification window are likely important, so why not include a supplemental figure that shows skill for a common verification window (1981-2020 for all leads)? Otherwise, the conclusions that can be drawn from this comparison are very limited.
L152: I think the conclusions about impact of initialization are somewhat shaky given the discrepancies in verification window (see above).
L187: I would delete “as it was assumed in the past”, because I don’t consider fast convergence to be a generally held assumption, particularly for the North Atlantic.
L213-215: Repetitive with introduction.
Fig. 4: Is it surprising that the AMOC climatology in DP_30yrs is almost identical to that from the DP (10-year), even at short leads, despite the different initialization methods?

Citation: https://doi.org/10.5194/egusphere-2025-1208-RC2
- AC2: 'Reply on RC2', Rashed Mahmood, 08 Sep 2025
  
  Please see the attached pdf file where we provide author response to the referee comments.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1208-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Publish subject to minor revisions (review by editor) (09 Sep 2025) by Gabriele Messori

AR by Rashed Mahmood on behalf of the Authors (09 Sep 2025) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (15 Sep 2025) by Gabriele Messori

AR by Rashed Mahmood on behalf of the Authors (22 Sep 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (29 Sep 2025) by Gabriele Messori

AR by Rashed Mahmood on behalf of the Authors (30 Sep 2025)

Journal article(s) based on this preprint

30 Oct 2025

Multi-decadal initialized climate predictions using the EC-Earth3 global climate model

Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes

Earth Syst. Dynam., 16, 1923–1934, https://doi.org/10.5194/esd-16-1923-2025,https://doi.org/10.5194/esd-16-1923-2025, 2025

Short summary

Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes

Supplement

https://doi.org/10.5194/egusphere-2025-1208-supplement

Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes

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Short summary

We present 30 year long initialized climate predictions run with the EC-Earth3 model. The predictions show high skill in most regions for near-surface temperatures, with some added skill from initialization for the first decade, but only very limited added skill beyond. The predictions exhibit drift associated with a persistent slowdown in Atlantic Meridonial Overturning Circulation , leaving the initialised predictions in a different climate state than the historical climate simulations.


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