the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Future heat extremes and impacts in a convection permitting climate ensemble over Germany
Marie Hundhausen
Hendrik Feldmann
Natalie Laube
Joaquim G. Pinto
Abstract. Heat extremes and associated impacts are considered the most pressing issue for German regional governments with respect to climate adaptation. We explore the potential of an unique high-resolution convection permitting (2.8 km), multi-GCM ensemble with COSMO-CLM regional simulations (1971–2100) over Germany regarding heat extremes and related impacts. We find an improved mean temperature beyond the effect of a better representation of orography on the convection permitting scale, with reduced bias particularly during summer. The projected increase in temperature and its variance favors the development of longer and hotter heat waves, especially in late summer and early autumn. In a 2° (3°) warmer world, a 26 % (100 %) increase in the Heat Wave Magnitude Index is anticipated. Human heat stress (UTCI > 32°C) and local-specific parameters tailored to climate adaptation, revealed a dependency on the major landscapes, resulting in significant higher heat exposure in flat regions as the Rhine Valley, accompanied by the strongest absolute increase. A non-linear, exponential increase is anticipated for parameters characterizing strong heat stress (UTCI > 32°C, tropical nights, very hot days). Providing local-specific and tailored climate information, we demonstrate the potential of convection permitting simulations to facilitate improved impact studies and narrow the gap between climate modelling and stakeholder requirements for climate adaptation.
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Marie Hundhausen et al.
Status: final response (author comments only)
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RC1: 'Comment on nhess-2022-283', Anonymous Referee #1, 27 Jan 2023
The present manuscript analyses temperature extremes and associated impacts through a high-resolution convection-permitting (2.8 km), multi-GCM ensemble with COSMO-CLM regional simulations from 1971 to 2100 over Germany. The study points out a projected increase in temperature and its variance combined with hotter and more lasting heat waves. The analysis also considers a comprehensive set of heat stress and user-tailored climate indices.
The research is surely relevant in terms of temperature extremes analysis and also for considering for the first time a multi-GCM ensemble of convection-permitting (CP) climate simulations over a multi-decadal time period.
The topic is surely fitting with the scope of the journal but different relevant aspects mainly pertaining to the methodological choices and technical aspects related to the simulations performed and analyzed in the study deserve clarification before being reconsidered for publication in NHESS journal.
General comments
- My first concern regards the very basis of the numerical simulation strategy adopted. I refer to the three-step nesting dynamical downscaling. It is well-known (e.g., Rummukainen 2010), the importance of a buffer or sponge zone of several grid nodes width between two nesting boundaries. This relaxation zone has the fundamental role of bringing the model solution towards the lateral boundary condition (LBC) fields diffusing (smoothing) the differences between the model solution and LBC. The sponge zone is characterized by a varying level of numerical instabilities. Coming to the simulations analyzed in the present study this buffer zone seems to be almost absent between the second (d02) and third (d03) nested domains. Subsequently, I expect a very noisy field driving d03 and I wonder if and to what extent this could negatively impact the proper development of d03 dynamics. Could the Authors provide any justifications for this quite atypical nesting strategy? And if any numerical detrimental effect has been detected or can be excluded.
- My second concern regards the bias correction. (i) from a technical point of view the quantile mapping (QM) configuration is not sufficiently described. I am especially referring to the correction of future time segments. As many studies point out empirical or parametric QM can affect the original climate change signal e.g., (Maraun 2016). So, it is a relevant choice to let QM free to alter the original simulated change signal, or conversely apply a trend-preserving QM configuration. Another relevant point is the extrapolation of the correction function over extreme values not present in the reference period but appearing in the future period. (ii) I do not get the meaning of bias adjusting convection-permitting (CP) scale simulations. This is not in general as we know they are still to some extent affected by processes misrepresentation but in the context of this study. Firstly, it is not clear when and how bias-adjusted simulations are considered in the analyses. I suggest making much clearer this point throughout the manuscript. Further, how we can disentangle the so-called added value of CP-scale simulations generated by an improved representation of physical processes and what is generated by the application of bias adjustment if raw and adjusted simulations analyses are not compared? This is especially when the same time segment is considered for deriving QM correction function and to evaluate it since the adjusted simulations and the observations will have by construction very similar statistical moments. Since CP-scale climate simulations are only very recently affordable and many aspects are still to be explored (like mechanisms and dynamics underlying hot extremes) I would rather focus on exploring the eventual added value and weaknesses of original CP-scale simulations compared to the (original) non-CP-scale simulations. This is just a suggestion I am not asking to rewrite a new manuscript. Also, for what concerns future changes, I would rather be interested in the influence of the CP-scale on eventual trend modification instead of using bias-adjusted CP-scale simulations since this latter could have modified original trends as well, especially considering extremes. Here, it is complicated to isolate the “real” effect of the high-resolution shuffling bias correction in this context. I believe that the manuscript already rises many relevant points even without including bias correction since represents another layer of uncertainty over statistics and climate indices that represent a rather high level of sophistication, even though not all the analyses involve bias-adjusted simulations (which increases confusion in the storytelling).
Specific comments
Line 4. “We find an improved mean temperature beyond the effect of a better representation of orography on the convection-permitting scale, with reduced bias, particularly during summer”. I do not believe that the manuscript analyses allow us to reach such a conclusion.
The caption of Figure 1. To me it results quite complicated to understand, please rephrase. Especially: “Nesting in (a) and model domain”
Section 2.2 should be improved (see general comments.).
Line 152. “user-oriented parameterizations are tested”. Please explain what you mean in this statement.
Lines 172-174. To me is not cleat the meaning of “reference humidity is constant at 20hPa” is. Please clarify.
Line 187. Please correct the quote's typo.
Figure 3. Instead of monthly means, I would rather compare the five daily temperature distributions (e.g., boxplots) or a percentile-based error to see which part of the distribution benefits the most from the higher resolution during the different parts of the year.
Line 208. Please clarify how the Wilcoxon test is applied in this context.
Line 210. Why is talk about trends here? This sentence is not clear to me.
Figure 4 caption. (c) appears twice.
Line 239. “Average variance” Perhaps ensemble variance?
Line 252. Please clarify the meaning of “the full width of half maximum (FWHM)”.
Figure 6. Why change color bar limits and colormap between (a) and (c) panels?
Heat wave characterization results section is quite loosely described, I would suggest better discussing this part.
Figure 7. caption, the description of the panel (d) is not clear to me. Also, PDFs are not described.
Discussion and conclusion section. Here it should be clarified how the CP-scale and/or the bias correction contribute to the reported improvement of temperature extremes representation.
Citation: https://doi.org/10.5194/nhess-2022-283-RC1 - AC1: 'Reply on RC1', Marie Hundhausen, 24 Mar 2023
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RC2: 'Comment on nhess-2022-283', Anonymous Referee #2, 31 Jan 2023
General comments:
The present study is dedicated to summer temperature, heat waves and associated implications for human health, agriculture and tourism in an ensemble of convection permitting regional climate model projections. In addition, the added value of the higher model resolution is demonstrated compared with a model version using parameterized convection. The study comprises three novel aspects at once: (1) a relatively large model data base of very high-resolution simulations over a quasi-transient forcing period, (2) the assessment of regional to local climate change patterns based on substantially improved model simulations, and (3) the consideration of derived climate indicators bridging the gap between meteorological heat events and socio-economic implications as well as adaptive requirements.
The paper represents a very valuable contribution to the community – with respect to methodical aspects (new model generation) and practice-relevant research (high-resolution patterns of climate change). Therefore, I recommend this manuscript to be accepted for publication in NHESS with minor revisions.
The minor revisions refer to a list of specific comments (see below) and to two general comments: (1) The manuscript basically is well presented, but exhibits some linguistic inaccuracies, especially typos. Therefore, I believe the authors themselves can achieve an improvement without explicit language editing by a native speaker. Nonetheless, a careful revision is required since the typos and inaccuracies are quite numerous.
(2) The GWL 2 and 3 periods seem to be associated with a lower level of temperature increase in central and southern Germany, at least in terms of the mean summer temperature. According to the IPCC and many other studies, I would have expected an above-average warming in Central Europe, given the fact that land masses are warming up stronger than the ocean surface, especially in the Northern Hemisphere extratropical regions (COWL pattern). Is summer less sensitive than the annual mean or is it an issue of the considered GCM-RCM combinations? I suggest that the authors pick up this point in their discussion.
Specific Comments:
Line 42: CPM stands for ‘convection permitting model’ (not convective).
Line 62: What is meant by quasi-transient? And ‘manor’ is certainly not the right word in this context, I guess it is ‘manner’.
Line 85: Table 2 is addressed in the text before this is done for Table 1.
Fig. 1: The fine lines in the background of the map seem to be river basin. Maybe a word is useful why these are plotted.
Line 96: I wouldn’t call it a climatological difference, when two three-year periods are compared with each other. Maybe the authors may want to call it what it is actually: a difference between three-year averages.
Subsection 2.1: I suggest to explain in few words the data sources and procedure leading to the HYRAS dataset and to explain what an equilibrium climate sensitivity is (Table 2).
Line 131: As this study is focussed on heat events, the question arises whether extreme temperature is indeed normal. There are several studies indicating that it is not, suggesting a combined QM approach with different statistical models below and above a temperature threshold. Please add a discussion on this issue.
Subsection 2.3.2: The description of UTCI is deficient. I either suggest to refer to the literature, leaving out all equations, or to provide a complete description with all terms figuring in the equations and the full equation for UTCI instead of f().
Line 207: It should be mentioned that this statement refers to the reanalysis-driven experiment. The enhanced spread is probably related to the fact that the model has a higher genuine resolution than the validation data, implying higher temperature differences in mountainous areas.
Fig. 4: Panel c is unclear to me: is it a comparison of the bias (then the caption is wrong saying that it is the 2.8 km minus 7 km scale) or does it indicate that the negative bias of the 7 km run is more or less compensated by the 2.8 km run. I would prefer seeing a bias reduction in panel c because it is more intuitive for the reader.
Beginning of section 4: I miss a statement about what model resolution is used for the subsequent analyses. I guess it is the 2.8 km scale since the bias could be reduced noticeably.
Line 247: Have the authors tested whether the density is indeed skewed left. At first sight, it looks quite normal.
Line 252: Please explain what FWHM actually tells us.
Line 277: At the end of this sentence the authors may include a ‘(not shown)’.
Fig. 9: It should be mentioned that the thick solid line refers to the ensemble mean. To be clear please add 50% ‘of the study region’.
Line 357: Why is the British model now claimed an outlier whereas previously it was not because blocking situations may be better represented in this model?
Citation: https://doi.org/10.5194/nhess-2022-283-RC2 - AC2: 'Reply on RC2', Marie Hundhausen, 24 Mar 2023
Marie Hundhausen et al.
Marie Hundhausen et al.
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