the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Patterns of changing surface climate variability from the Last Glacial Maximum to present in transient model simulations
Abstract. As of 2023, global mean temperature has risen by about 1.45 ± 0.12 °C with respect to the 1850–1900 pre-industrial baseline according to the World Meteorological Organization. This rise constitutes the first period of substantial global warming since the Last Deglaciation, when global temperatures rose over several millennia by about 4.0–7.0 °C according to proxy reconstructions. Similar levels of warming could be reached in the coming centuries considering current and possible future emissions. Such warming causes widespread changes in the climate system of which the mean state provides only an incomplete picture. Indeed, climate’s variability and the distributions of climate variables change with warming, impacting for example ecosystems and the frequency and intensity of extremes. However, climate variability during transition periods like the Last Deglaciation remains largely unexplored.
Therefore, we investigate changes of climate variability on annual to millennial timescales in fifteen transient climate model simulations of the Last Deglaciation. This ensemble consists of models of varying complexity, from an energy balance model to Earth System Models and includes sensitivity experiments, which differ only in terms of their underlying ice sheet reconstruction, meltwater protocol, or consideration of volcanic forcing. While the ensemble simulates an increase of global mean temperature of 3.0–6.6 °C between the Last Glacial Maximum and Holocene, we examine whether common patterns of variability emerge in the ensemble. To this end, we compare the variability of surface climate during the Last Glacial Maximum, Deglaciation and Holocene by estimating and analyzing the distributions and power spectra of surface temperature and precipitation. For analyzing the distribution shapes, we turn to the higher order moments of variance, skewness and kurtosis. These show that the distributions cannot be assumed to be normal, a precondition for commonly used statistical methods. During the LGM and Holocene, they further reveal significant differences as most simulations feature larger variance during the LGM than Holocene, in-line with results from reconstructions.
As a transition period, the Deglaciation stands out as a time of high variance of surface temperature and precipitation, especially on decadal and longer timescales. In general, this dependency on the mean state increases with model complexity, although there is a large spread between models of similar complexity. Some of that spread can be explained by differences in ice sheet, meltwater and volcanic forcings, revealing the impact of simulation protocols on simulated variability. The forcings affect variability not only on their characteristic timescales, rather, we find that they impact variability on all timescales from annual to millennial. The different forcing protocols further have a stronger imprint on the distributions of temperature than precipitation. A reanalysis of the LGM exhibits similar global mean variability to most of the ensemble, but spatial patterns vary. However, whether current paleoclimate data assimilation approaches reconstruct accurate levels of variability is unclear. As such, uncertainty around the models’ abilities to capture climate variability likewise remains, affecting simulations of all time periods, past, present and future. Decreasing this uncertainty warrants a systematic model-data comparisons of simulated variability during periods of warming.
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RC1: 'Comment on egusphere-2024-1396', Anonymous Referee #1, 07 Jul 2024
Review of "Patterns of changing surface climate variability from the Last Glacial Maximum to present in transient model simulations" by Ziegler et al.
The manuscript presents an analysis of global climate variability during the last deglaciation based on multi-model and reanalysis data. The authors collected climate model simulations of the last deglaciation with different climate models of different levels of complexities or experimental protocols. The authors analyzed global temperature and precipitation variabilities using multiple indicators for variabilities. The authors found increased climate variabilities during the last deglaciation than the LGM or Holocene with specific timescales and regions. The authors also find that the variability during the last deglaciation is affected by the complexity of the climate models or experimental design protocol.
I think this study's topic is well-suited for Climate of the Past, and the method and analysis of this study, particularly for introducing multiple variability indicators and analyzing both temperature and precipitation, is unique. However, the manuscript needs additional work to improve the readability particularly for the following two points. Firstly, many figure panels, including supplemental figures (S41), are referenced in the manuscript (but some supplemental figures are not referenced), making it hard to follow. A multi-model study with global analysis may need many figure panels, but I had tough time understanding figures (Do Figs 6-11 need 24 or 27 panels?). I wonder if there is a better way to show figures in a more structured way to help readers. Secondly, the introduction section seems to lack information on what has been done regarding climate variability during the last deglaciation and what the knowledge gap is. As in the discussion section of this manuscript, there's a proxy study (e.g. Rehfeld et al. 2018) and climate modelling study (e. g. Zhu et al. 2019; Shi et al. 2022) on climate variability during the LGM or the last deglaciation. I think their methodology and results can be summarized in the introduction, and the authors can clarify what knowledge is lacking and what this study's strengths are. I also think stating a hypothesis in the introduction will help clarify the key points of this study.Specific Comments:
L8-L9: The phrase "largely unexplored" might be too general. This sentence can be more specific based on previous knowledge gap or strength of this study.L27-L28: I'm not sure what is unclear. Do you mean it is unclear whether LGMR (Osman et al. 2021) simulates accurate spatial patterns of climate variability?
L72-L79: I'm not sure what the point of this paragraph is. I wonder if L71 and L80 can be directly connected to state the importance of climate variability and what proxy says on climate variabilities in the last deglaciation.
L134-L140: I understand that one strength of skewness is that it can be an indicator of abrupt climate change, according to this paragraph. There would be a discussion paragraph on whether skewness in the deglaciation simulations can be an indicator of abrupt climate changes.
L141-L151: As far as I understand, applying skewness and kurtosis to paleoclimate is new in this study, which can be emphasized.
L181: Is dd/m always used as GMP, global mean precipitation? Please clarify.
L187: I don't understand what is different between MPI-ESM r1&r6 and r2&r5, as all columns in Table 1 are the same. Are they from simulations with different model parameters in Kapsch et al. (2022)? One way is to add a reference column in Table 1.
L465 & L470 "centennial" instead of "decadal and centennial"? Because Figure 6 say centennial standard deviation.
L513-L523: Based on Figures 7, 8 it is discussed that volcanic forcing impacts skewness and kurtosis during Holocene based on MPI-ESM r6 and r7 simulations. However, Figures 7, 8 and S12 make me feel that with the volcanic forcing, MPI-ESM simulations resemble HadCM3 simulations despite HadCM3 not having volcanic forcing. Are there any discussions for this model difference?
L614-L618: Is it because (a) volcanic forcing or inter-annual to centennial variability, or (b) volcanic forcing does not correspond to timescale variation, but it can induce inter-annual to centennial variability?
L694-L714 and Figure 13: I couldn't understand the point of this subsection, and why Figure 13 is necessary for discussing variability uncertainty. Please add some introduction.
L735-737: You mean that the meridional temperature gradient is enhanced during LGM as in Shi et al. (2022), but the variance is not increased like Shi et al. (2022)? If so, isn't it a significant result worth emphasizing and discussing further?
L744-L745: In addition to long-term memory, there's transient forcing during 23 to 19 ka (Ivanovic et al. 2016), unlike equilibrium LGM simulation at 21ka.
L884 (minor): Why "using an EMIC more focused on atmospheric dynamics" , unlike [using a GCM when focusing on climate variability]
L892-L893 (minor): Each simulation from previous articles used in this study focused specifically on the atmosphere or ocean processes of the last deglaciation, which is one primary reason the model complexity or experimental design differs. Even so, it's a great opportunity to discuss good choices on the scientific question of climate variability.
L895-L925 (minor): The sentences overlap with the first paragraph of the discussion section. Please consider describing brief conclusions. (or merged with the discussion section? )
Figure 1a: EPICA Dome C (Jouzel et al. 2007) and NGRIP (Andersen et al. 2004) presents local temperature change at the ice core site, so it looks strange the vertical axis is represented as GMST. Please clarify the vertical axis.
Figure 1c: this panel presents sea-level change, but it would fit better including meltwater input as the timeseries of meltwater. While meltwater input would differ between models (e.g. Snoll et al., 2024), it provides an essential information as the meltwater is discussed as the critical factor in climate variabilities.Figure 12: What does "PMIP3" mean? Does it come from Li et al. (2013)? Please clarify in the caption and results section.
Citation: https://doi.org/10.5194/egusphere-2024-1396-RC1 -
CC1: 'Reply to RC1', Kira Rehfeld, 15 Jul 2024
We thank the reviewer for her/his detailed reading. Please find our response attached.
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AC2: 'Reply on RC1/CC1', Elisa Ziegler, 16 Jul 2024
Please find our reply in https://doi.org/10.5194/egusphere-2024-1396-CC1
Citation: https://doi.org/10.5194/egusphere-2024-1396-AC2
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AC2: 'Reply on RC1/CC1', Elisa Ziegler, 16 Jul 2024
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CC1: 'Reply to RC1', Kira Rehfeld, 15 Jul 2024
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RC2: 'Comment on egusphere-2024-1396', Anonymous Referee #2, 12 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1396/egusphere-2024-1396-RC2-supplement.pdf
- AC1: 'Reply on RC2', Elisa Ziegler, 15 Jul 2024
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CC2: 'Comment on egusphere-2024-1396', Michael Sigl, 23 Jul 2024
Thanks for this contribution. I just would like to make small technical correction. It appears from your Table 1 that you are using two transient simulations that include volcanic forcing (MPI-ESM r7 and TransEBM). In Figure 1 you summarize the used volcanic forcing under the term “TephraSynthIce” (Schindlbeck et al., 2023).
Studying Figure 1 next to the two original volcanic forcing reconstructions (attached as a pdf), I assume that the forcing that was used here was derived by merging of the two independent volcanic reconstructions (PalVol, based on tephra records, Schindlbeck et al., 2024) and HolVol (based on ice-core sulfur records, Sigl et al. 2022) as was proposed to do for such purposes by Schindlbeck et al. (2024).
It would be helpful to clarify this here and provide details of where exactly these two reconstructions have been merged and to update Figure 1, Table 1, references and the Supplement with the relevant information. Also, the reference to the Schindlbeck et al., (2023) paper in discussion should be updated to the final published paper.
With best regards.
Michael Sigl
References:
Schindlbeck-Belo, J. C., Toohey, M., Jegen, M., Kutterolf, S., and Rehfeld, K.: PalVol v1: a proxy-based semi-stochastic ensemble reconstruction of volcanic stratospheric sulfur injection for the last glacial cycle (140000–50 BP), Earth Syst. Sci. Data, 16, 1063-1081, 2024.
Sigl, M., Toohey, M., McConnell, J. R., Cole-Dai, J., and Severi, M.: Volcanic stratospheric sulfur injections and aerosol optical depth during the Holocene (past 11 500 years) from a bipolar ice-core array, Earth Syst. Sci. Data, 14, 3167-3196, 2022.
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AC3: 'Reply on CC2', Elisa Ziegler, 24 Jul 2024
Dear Michael Sigl,
thank you for this clarification. Indeed, the "TephraSynthIce" volcanic reconstruction we referred to combines the "HolVol" reconstruction (Sigl et al., 2022) as well as the "PalVol" reconstruction drawing on Tephra records and including synthetic volcanic eruptions to mitigate underestimation of small eruptions. We will update the reference to Schindlbeck (2023) to the final paper and further include Sigl. et al., 2022.
Wit best regards,
Elisa ZieglerCitation: https://doi.org/10.5194/egusphere-2024-1396-AC3
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AC3: 'Reply on CC2', Elisa Ziegler, 24 Jul 2024
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