the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ModE-Sim – A medium size AGCM ensemble to study climate variability during the modern era (1420 to 2009)
Eric Samakinwa
Laura Lipfert
Stefan Brönnimann
Abstract. We introduce ModE-Sim, a medium size ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR version (T63/approx. 1.8° horizontal with 47 vertical levels) that covers the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009 ModE-Sim consists of 31620 simulated years in total. The dataset forms the input for a data assimilation procedure that combines historical climate informations with additional constraints from a climate model to produce a novel gridded 3-dimensional dataset of the modern era. Additionally, ModE-Sim on its own is also suitable for many other applications as its various subsets can be used as initial condition and boundary condition ensemble to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation of the two key variables 2m-temperature and precipitation. We demonstrate ModE-Sims ability to represent their mean state, to produce a reasonable response to external forcings and to sample internal variability. At the example of heat waves we show that the ensemble is even capable of capturing extreme events.
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Ralf Hand et al.
Status: open (until 21 Jun 2023)
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RC1: 'Comment on egusphere-2023-209', Anonymous Referee #1, 26 May 2023
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Summary: The manuscript presents a new dataset of climate simulations spanning approximately five hundred years, generated by the global atmospheric model of the Max-Blank Institute. This ensemble of simulations incorporates variations in external forcing and estimated sea-surface temperature and sea ice cover. The authors assess the realism of these ensembles in reproducing the mean climate, climate variations, and the frequency of heat waves. The manuscript is generally well-written, with clear figures and simulation descriptions. However, there are some concerns with specific sections, such as the one discussing heat waves. Additionally, there are some general comments for the authors to consider in the revised version.
Main points:
1) The manuscript should emphasize that these ensembles cannot capture the internal climate variability originating in the ocean. This is mentioned in the text, but it remains somehow hidden, and at some stages, it may be misleading. This point should be highlighted prominently, even in the abstract and title, as the simulations are driven on observed or reconstructed sea surface temperatures without including the possible internal variability.
2) The section on heat waves needs improvement. The section's title is misleading, as it generally refers to extreme events, not specifically heat waves. The definition of heat waves should be provided in the main text rather than in the caption of Figure 7. The underestimation of heat wave frequency and intensity before 1920 should be acknowledged more explicitly, as it is not a slight discrepancy. The text should avoid suggesting that most heat waves are caused directly by external radiative forcing, as Figure 7 indicates that they are primarily related to mean temperatures. To compare the model's ability to capture extreme events, model biases should be corrected, and heat waves could be defined separately in the reanalysis and model data. This can also be achieved by defining the 95% percentile for reanalysis and for model data separately.
Specific points:
3. The information about data assimilation and a gridded 3-dimensional dataset may not be relevant enough for this manuscript for inclusion in the abstract.
4) The sentence claiming the ensemble's capability to capture extreme events (heat waves) needs careful revision based on the aforementioned points.
5) The phrase "sampling of the supposed true state" is unclear. Clarification is needed, possibly by reformulating the sentence to avoid ambiguity. If by 'true state' the authors mean the mean climate, then this is not a random variable. It is a fixed parameter of the Earth's climate. However, if by 'true state' the authors mean the climatic probability distribution, then the sentence makes sense.
6) Section 2.2 should mention the external forcing used for the simulations upfront rather than referring to it later in the text.
7) In line 108, "set 1430-2" should likely be corrected to "set 1430-3."
8) '' forcing and the ocean boundary conditions can be detected from the subensemble means computed from each 20-member set separately (Fig. 2b & c), indicating that the ensemble size is clearly sufficient to separate forced signals from internal variability'
The distinction between internal atmospheric variability and internal climate variability with oceanic origins is important and should be clarified. The current phrasing might suggest that the ensemble captures all internal climate variability, which is not the case.
Citation: https://doi.org/10.5194/egusphere-2023-209-RC1
Ralf Hand et al.
Ralf Hand et al.
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