the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Oct 2023
The climate of the Eastern Mediterranean and the Nile River basin 2000 years ago using the fully forced COSMO-CLM simulation
Abstract. Understanding the past climate at regional scale, the impact of natural variability and sensitivity by studying the underlying dynamics and processes, can provide a point of reference for future climate conditions under anthropogenic forcing. The Eastern Mediterranean (EM) and Nile River basin (NR) regions are of particular interest for the study of past climate due to their location under the influence of major atmospheric teleconnections. We developed a high-resolution regional model for paleoclimate applications, COSMO-CLM, by integrating all external forcings and conducted a transient simulation from 500 BCE to 1850 CE. Principal Component Analysis (PCA) was applied for winter/summer precipitation and temperature to validate the model set up and showed very good agreement between simulated and observational/reanalysis data. Further, 400–362 BCE and 1800–1850 CE have been selected for the comparison of the mean climate conditions of the early Roman period (ERP) and pre-industrial times (PI). The comparison of temperature and precipitation suggests comparable mean climatic conditions with spatial differences in terms of variability within the study regions. Over the Eastern Mediterranean (EM), ERP is wetter and warmer in both winter and summer compared to PI, with higher variability in temperature and precipitation in summer than in winter. In the Nile River basin (NR), ERP summers were wetter and more variable compared to PI. The ERP over NR is warmer by approximately 0.5 °C in winter and cooler by 0.5 °C in summer, with low variability in winter and high variability in summer compared to PI. The relevant large-scale circulation of the two periods shows consistent spatial structures with the corresponding precipitation/temperature EOF patterns, albeit with varying amplitudes. The 2500 years transient simulation sheds light to the paleoclimate conditions and relevant atmospheric circulation as well as processes of periods of interest in complex areas with detailed output and comprehensive forcing allowing for better representation of the regional climate variability and change. Comparison of simulated output with proxy records, reconstructions and detailed studies of specific events, e.g., volcanic eruptions, can help to capture the spatiotemporal extent of these events and their impact on climate variability and change, in addition to providing insights into their impact on societal change and human history.
- Preprint
(5436 KB) - Metadata XML
- BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on cp-2023-77', Anonymous Referee #1, 21 Nov 2023
Review of “The climate of the Eastern 1 Mediterranean and the Nile River basin
2000 years ago using the fully forced COSMO-CLM simulation” by Zhang et al.
This study is a first attempt to mobilize long transient regional climate simulation to understand past climate changes. The study focuses on changes in precipitation and mean 2 m temperature within the Eastern Mediterranean (EM) and Nile River (NR) basin regions from 500 BCE to 1850 CE. The authors evaluate the RCM's capability to simulate homogeneous regions during winter (DJF) and summer (JJA) under current climate conditions (1980-2018), then compare precipitation and 2 m temperature between the Roman period (ERP: 400-362 BCE) and the pre-industrial period (PI: 1800-1850 CE), as well as changes in large scale teleconnection patterns.
I am convinced that dynamical downscaling is a valuable tool to refine coarse GCM outputs to improve our understanding of past climate changes, especially in regions with diverse topography. While the scientific significance is excellent, the methodology used to identify homogeneous regions is not straightforward and the presentation quality deserves to be improved by selecting a subset of results and further highlighting the main take-home messages. Below are a couple of major and minor comments that require consideration for the paper's acceptance for publication in Climate of the Past.
Major Comments:
- While the study focuses on winter and summer, the most contrasting seasons, the primary changes between ERP and PI predominantly occur in spring and late summer-fall, especially concerning the mean state (Fig. 7). Including variability (standard deviation for each month for each period and inter-period difference) would provide a more comprehensive understanding of the changes. The authors are encouraged to consider analyzing or discussing the main modes of variability and associated large-scale teleconnections for these seasons as well.
- The paper lacks a discussion on the added value of dynamical downscaling compared to the forcing GCM. A comparison of the two solutions in terms of spatial and temporal characteristics of homogeneous regions (e.g., Figs. 2-3) and annual cycles (Fig. 7) would help illustrate the usefulness of long transient RCM simulations for paleoclimate.
- Changes in annual cycle and teleconnection patterns presented in Section 3.2 could gain in robustness by evaluating the RCM’s capability to simulate these characteristics under current climate conditions. Fig. 7 should be duplicated for the 1980-2018 period (Obs + RCM + GCM) and included at the beginning of the evaluation section. Teleconnections should also be evaluated for the 1980-2018 period in the Supplementary Material and briefly discussed in the main text.
- Regarding the EOF approach, there are a couple of questions:
- The rationale for retaining only grid points with positive values for identifying homogeneous region needs clarification, considering that EOF loadings can include strong negative and positive values simultaneously e.g. in the case of a dipole pattern. The possibility of one specific grid point being classified in more than one region should be addressed, along with the sensitivity to the percentile threshold.
- Considering the lengthy model evaluation section, it might be advantageous to perform one EOF analysis (or a more straightforward method: see my next comment) for the entire region (all grid points within the EM-NR region) and the entire annual cycle (monthly anomalies) simultaneously. Alternatively, some results could be moved to the Supplementary Material to shorten the section and to let room for a discussion on RCM added values compared to its forcing GCM.
- The EOF approach is not straightforward to identify homogeneous regions in terms of temporal variability. As illustrated in Figures 3 and 5, it becomes apparent that certain regions exhibit highly similar temporal variability. I believe the paper could gain valuable insights by either transitioning to or, at least, discussing clustering analyses (e.g., k-means, hierarchical clustering) since these techniques have proved to efficiently identify regions with shared temporal variability.
- The rationale behind performing rotated EOFs for the present-day period while classical EOFs for the PI and ERP periods should be clarified.
- Important details about the RCM simulation are missing in Section 2.1, such as the horizontal resolution and more information on changes in forcings, particularly land cover changes over the 2500-year period. Additionally, the high correlation of simulated precipitation's temporal variability with observations (Figs. 3 and 5) requires clarification, given the RCM simulation is forced with outputs from an ocean-atmosphere coupled GCM.
Minor comments
L126: Which forcings are implemented in the RCM?
L82-87: It may be worth indicating that the Holocene and Anthropocene are not analogues. Climate change in the former is primarily driven by orbital changes, while climate change in the latter is mainly driven by anthropogenic forcing. D'Agostino et al. (2019: https://doi.org/10.1029/2018GL081589) also demonstrated that the physics of changes differ between the two periods.
L222: "and eventually in each data set". Do you mean that 75% of the variance is explained by the first 6 EOF modes regardless of the dataset? Does this value stand for both precipitation and temperature? Please clarify.
L231: "mean values and standard deviations". Do you mean "mean spatial patterns and their variability"?
L246-249:
- The spatial correlation is 0.91 for winter and 0.94 for summer.
- Spatial correlations between GPCC and CCLM are not discussed. Please discuss them or consider removing them from Table 1.
- It may be worth indicating that there is no 1-to-1 correspondence between the EOF ranking between CRU and CCLM. This means that while the patterns are accurately captured by the regional model, their contribution to the total variance of precipitation differs from the observations.
L250: In addition to Table 1, it would be helpful to show the EOF loadings in the Supplementary Material.
L254-255: It may be worth giving another example since GPCC-CRU spatial correlations are not shown in Table 1. For instance, region 1 in Fig. 2a-c corresponds to the grid points contributing the most to EOF1 for CRU and GPCC and to EOF4 for CCLM.
L279-280: This information should be stated in an introductory paragraph of section 3.1.
Figure 3: This figure shows precipitation anomalies, but I wonder why the anomaly is systematically computed as the departure between each season of each dataset and the CRU climatology. Why not use the climatology of each dataset?
L344-345: Already stated in the Figure caption. No need to repeat in the main text.
L351-354: The results with and without orbital forcing are not similar for temperature. I'm surprised that precipitation changes to be the same with or without orbital forcing. This may indeed be due to area-averaging or because of internal variability. I wonder if this conclusion would remain unchanged considering low-passed filtered timeseries.
Figure 7: Please modify the colors of the two curves in orange because they do not allow for a clear distinction between the two periods. Also, modify the legend to avoid confusion between the left and right ones (e.g., ERP: BCE400-362 and PI: 1800-1850).
L363-365: Most of these differences do not reach the 95% confidence level.
L373-374: During the winter, most of the NR experienced no significant changes between the two periods. Differences are very close to 0 except in western Ethiopia.
L375-376: The wetter summer conditions in the NR region during ERP could relate to a stronger inter-hemispheric gradient at this time compared to PI, as suggested by Fig. 8 (bottom panel). This would be consistent with recent studies based on global transient simulations for the last 6000 years.
L416-419: Please add references.
Figure 9: How are these patterns obtained? By reconstructing rainfall anomalies based on EOF1/2/3? Please clarify.
Citation: https://doi.org/10.5194/cp-2023-77-RC1 -
AC1: 'Reply on RC1', Mingyue Zhang, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-AC1-supplement.pdf
-
RC2: 'Comment on cp-2023-77', Anonymous Referee #2, 11 Dec 2023
Review Zhang et al., “The climate of the Eastern Mediterranean and the Nile River basin 2000 years ago using the fully forced COSMO-CLM simulation”.
General comments
The paper by Zhang et al. presents results from a transient climate simulation covering 500 BCE to 2018 CE. This simulation has been performed with the paleoclimate version of the COSMO-CLM regional climate model (RCM), which was forced with a global simulation carried out with the MPI-ESM-LR model. Such a transient simulation with an RCM is novel and provides a spatially detailed view of the climate. In this paper, the RCM is set up for a domain spanning the Nile River basin and the eastern part of the Mediterranean. The results section consists of two parts. In the first part, the model’s performance is evaluated by comparing the simulated climate for the period 1980-2018 with observations and reanalysis data for the same time frame. This comparison indicates that the model is doing a reasonable job, except for the precipitation in the Nile River domain. The second part provides a comparison of the results for two periods (the Early Roman Period (ERP, 400-362 BCE) and the preindustrial period (PI, 1800-1850 CE)), and presents also an analysis of the link between temperature and precipitation on the one hand, and the atmospheric circulation on the other hand.
Although the performed simulation is innovative and represents a technological improvement that could play an important role in studies of past climates, there are several issues with this paper that need to be resolved before it can be published. These issues are discussed in detail below, and relate to the purpose of the paper, the set-up of the simulation and the analysis of the results.
Main comments
- Purpose of the paper. It is not clear to me what the purpose of this paper really is. One goal is to provide an evaluation of the performance of the model for the present-day climate, but this evaluation is not presented as being the main aim. The results for the ERP and PI periods are compared, and it may be expected that a characterization of the ERP climate is the main objective of this paper, as the PI period is usually taken as the reference period in paleoclimate modelling. However, the introduction section of this paper does not even mention the Early Roman Period once, and thus does not explain at all why there is a focus on the ERP. My advice is therefore to streamline the introduction section and make a case why it is important to obtain a more detailed climatological understanding of the ERP. This should include an explanation of why the 400-362 BCE period is chosen. Why not another period, and why these specific 38 years? To my knowledge, there is no established definition of the Early Roman Period that falls between 400 and 362 BCE, so this requires a thorough discussion.
- Set-up of the simulation - CCLM. The results are obtained with the newly developed paleoclimate version of the CCLM model. However, it is not explained what has been modified compared to the “normal” version. More details on the model should thus be provided. What is meant by the phrase “by implementing CCLM in model version 5.0 with CLM version 16”? this phrase suggests that CCLM is a different model from CLM, but CCLM just stands for COSMO-CLM, doesn’t it? And what is actually the spatial resolution of the version of CCLM applied here?
- Set-up of the simulation – applied forcings. Several forcings are applied in the simulation, but it is not clear what the main differences between these forcings are. The authors mention orbital, solar, GHG, volcanic and land-use changes. I suggest explaining what the main differences in radiative forcings are between the main periods of interest in this paper: present period, PI and ERP. How do each of these radiative forcings change between the three periods? What forcings are most important, and how large are the differences when expressed in Wm-2? This information is important for interpreting the results and should be included.
- Analysis of the results. The differences between the ERP and PI climates should be discussed in terms of the differences in radiative forcings between the two periods (see point 3 above). In addition, it should become clear how the results for the ERP compare to proxy-based climate reconstructions for the same period. On line 497 it is noted that “comparing modeled data with proxy records is essential for a comprehensive understanding of climate variability”, but surprisingly the authors do not make such a comparison for the results presented in this paper. This is clearly a missed opportunity, and in my opinion a model-proxy comparison should be included. Moreover, the model results should also be discussed more extensively relative to previous modelling studies.
- Discussion. The present paper presents the first transient RCM simulation of the past 2500 years, so one question to answer is if there is a clear advantage of making the effort of running an RCM in transient mode. If the authors would run three snapshot RCM experiments for present-day, PI and ERP, to what extent would the results be different? In other words, what have we learned about the ERP climate from the transient simulation that could not be obtained from a snapshot experiment with mean forcings for the ERP?
Other comments
General: I suggest indenting the first line of a paragraph to improve readability.
p. 1, Title. I find including “2000 years ago” in the title misleading, as this suggests that the climate of the first century CE is studied, but this is not the case. It would be clearer to refer to the ERP. The title is also missing a verb. So, I propose revising the title, for instance: “The climate of the Eastern Mediterranean and the Nile River basin in the Early Roman Period (400-362 BCE) as simulated in a fully forced COSMO-CLM experiment”.
p. 1, line 17. “… of particular interest due to their location under the influence of major atmospheric teleconnections.” I suggest mentioning here that the region is also of interest because of its important role in human history.
p. 1, lines 29-30. Should be “sheds light on”. And “paleoclimate” is one word. What is meant by “processes of periods of interest”?
p. 1, I suggest removing the example on volcanic eruptions from the abstract as the impact of volcanic forcing is not specifically studied in this paper.
p. 2, lines 37-45. This first paragraph is not easy to read because it seems to consist of single sentences without a clear connection. For instance, what is the connection between the “significant changes” in the first sentence and the “anthropogenic climate change” in the second sentence? Moreover, what is meant by “historical context” in the third sentence in relation to this “anthropogenic climate change”? And what is the connection between “paleoclimatic data” and the “historical context”? I suggest rewriting this first paragraph and be more specific.
p. 2, line 64: Start new paragraph here that is focused on NR.
p. 3, lines 82-84. Repetition, pleas revise.
p.3, line 112. “Climate variations are also related to changes in external forcing…”. What does “also” refer to here? This is not clear. To internal climate variability?
p. 3, line 113. “The climate system is primarily driven by solar radiation, and the variations of solar irradiance can lead to changes at decadal to centennial time scales.” I agree that the sun is the main source of energy for the climate system, but the other external forcings are also important drivers of climate system variability. This also depends on the timescale involved. For example, orbital forcing is dominant at longer paleoclimate timescales. In addition, there are important internal modes of variability such as ENSO. My suggestion is to revise this part.
p. 4, line 119. “Historical studies have matched the Nile flooding with the impact of volcanic eruptions”. How was this matched? What happened to the Nile discharge following a large volcanic eruption? Please explain.
p. 4, line 121. “…effects on the global climate”. What effects are meant here? Please clarify.
p. 4, Lines 143-146. This paragraph can be removed in my opinion, because it does not really provide useful information, as the structure of the paper just follows what is common in scientific literature.
p. 5, line 161. I guess “COSMO-CCLM” should be replaced by either “CCLM” or “COSMO-CLM”?
p. 5, line 163. Are the GHG concentrations based on ice cores?
p. 8, line 248. “.. the highest significant correlation of 0.94 for winter (DJF) and summer (JJA).” Where in Table 1 do we see this 0.94 correlation for the winter season?
p. 8, lines 255-256. “For example, GPCC winter region 5 corresponds to GPCC REOF11 as the counterpart to CRU REOF5”. I do not understand how this works. How does this follow from Table 1 and/or Figure 2? Please explain.
Figures 2 and 4. The colors in these figures are difficult to see and hard to match with the colors in the legend. And I am sure it is impossible to interpret these figures for readers who are (partly) color blind, so I propose finding another solution. Especially Figure 4 is not useful, other than that it shows that CCLM is not performing well for this region.
Figures 3 and 5. What do the percentages signify?
Figure 7. The font used within the figure is too small.
Figure 8. I propose to only plot the seasonal differences where they are statistically significant, instead of using the dotted areas.
Citation: https://doi.org/10.5194/cp-2023-77-RC2 -
AC2: 'Reply on RC2', Mingyue Zhang, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-AC2-supplement.pdf
-
RC3: 'Comment on cp-2023-77', Anonymous Referee #3, 11 Dec 2023
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-RC3-supplement.pdf
-
AC3: 'Reply on RC3', Mingyue Zhang, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-AC3-supplement.pdf
-
AC3: 'Reply on RC3', Mingyue Zhang, 16 Feb 2024
Status: closed
-
RC1: 'Comment on cp-2023-77', Anonymous Referee #1, 21 Nov 2023
Review of “The climate of the Eastern 1 Mediterranean and the Nile River basin
2000 years ago using the fully forced COSMO-CLM simulation” by Zhang et al.
This study is a first attempt to mobilize long transient regional climate simulation to understand past climate changes. The study focuses on changes in precipitation and mean 2 m temperature within the Eastern Mediterranean (EM) and Nile River (NR) basin regions from 500 BCE to 1850 CE. The authors evaluate the RCM's capability to simulate homogeneous regions during winter (DJF) and summer (JJA) under current climate conditions (1980-2018), then compare precipitation and 2 m temperature between the Roman period (ERP: 400-362 BCE) and the pre-industrial period (PI: 1800-1850 CE), as well as changes in large scale teleconnection patterns.
I am convinced that dynamical downscaling is a valuable tool to refine coarse GCM outputs to improve our understanding of past climate changes, especially in regions with diverse topography. While the scientific significance is excellent, the methodology used to identify homogeneous regions is not straightforward and the presentation quality deserves to be improved by selecting a subset of results and further highlighting the main take-home messages. Below are a couple of major and minor comments that require consideration for the paper's acceptance for publication in Climate of the Past.
Major Comments:
- While the study focuses on winter and summer, the most contrasting seasons, the primary changes between ERP and PI predominantly occur in spring and late summer-fall, especially concerning the mean state (Fig. 7). Including variability (standard deviation for each month for each period and inter-period difference) would provide a more comprehensive understanding of the changes. The authors are encouraged to consider analyzing or discussing the main modes of variability and associated large-scale teleconnections for these seasons as well.
- The paper lacks a discussion on the added value of dynamical downscaling compared to the forcing GCM. A comparison of the two solutions in terms of spatial and temporal characteristics of homogeneous regions (e.g., Figs. 2-3) and annual cycles (Fig. 7) would help illustrate the usefulness of long transient RCM simulations for paleoclimate.
- Changes in annual cycle and teleconnection patterns presented in Section 3.2 could gain in robustness by evaluating the RCM’s capability to simulate these characteristics under current climate conditions. Fig. 7 should be duplicated for the 1980-2018 period (Obs + RCM + GCM) and included at the beginning of the evaluation section. Teleconnections should also be evaluated for the 1980-2018 period in the Supplementary Material and briefly discussed in the main text.
- Regarding the EOF approach, there are a couple of questions:
- The rationale for retaining only grid points with positive values for identifying homogeneous region needs clarification, considering that EOF loadings can include strong negative and positive values simultaneously e.g. in the case of a dipole pattern. The possibility of one specific grid point being classified in more than one region should be addressed, along with the sensitivity to the percentile threshold.
- Considering the lengthy model evaluation section, it might be advantageous to perform one EOF analysis (or a more straightforward method: see my next comment) for the entire region (all grid points within the EM-NR region) and the entire annual cycle (monthly anomalies) simultaneously. Alternatively, some results could be moved to the Supplementary Material to shorten the section and to let room for a discussion on RCM added values compared to its forcing GCM.
- The EOF approach is not straightforward to identify homogeneous regions in terms of temporal variability. As illustrated in Figures 3 and 5, it becomes apparent that certain regions exhibit highly similar temporal variability. I believe the paper could gain valuable insights by either transitioning to or, at least, discussing clustering analyses (e.g., k-means, hierarchical clustering) since these techniques have proved to efficiently identify regions with shared temporal variability.
- The rationale behind performing rotated EOFs for the present-day period while classical EOFs for the PI and ERP periods should be clarified.
- Important details about the RCM simulation are missing in Section 2.1, such as the horizontal resolution and more information on changes in forcings, particularly land cover changes over the 2500-year period. Additionally, the high correlation of simulated precipitation's temporal variability with observations (Figs. 3 and 5) requires clarification, given the RCM simulation is forced with outputs from an ocean-atmosphere coupled GCM.
Minor comments
L126: Which forcings are implemented in the RCM?
L82-87: It may be worth indicating that the Holocene and Anthropocene are not analogues. Climate change in the former is primarily driven by orbital changes, while climate change in the latter is mainly driven by anthropogenic forcing. D'Agostino et al. (2019: https://doi.org/10.1029/2018GL081589) also demonstrated that the physics of changes differ between the two periods.
L222: "and eventually in each data set". Do you mean that 75% of the variance is explained by the first 6 EOF modes regardless of the dataset? Does this value stand for both precipitation and temperature? Please clarify.
L231: "mean values and standard deviations". Do you mean "mean spatial patterns and their variability"?
L246-249:
- The spatial correlation is 0.91 for winter and 0.94 for summer.
- Spatial correlations between GPCC and CCLM are not discussed. Please discuss them or consider removing them from Table 1.
- It may be worth indicating that there is no 1-to-1 correspondence between the EOF ranking between CRU and CCLM. This means that while the patterns are accurately captured by the regional model, their contribution to the total variance of precipitation differs from the observations.
L250: In addition to Table 1, it would be helpful to show the EOF loadings in the Supplementary Material.
L254-255: It may be worth giving another example since GPCC-CRU spatial correlations are not shown in Table 1. For instance, region 1 in Fig. 2a-c corresponds to the grid points contributing the most to EOF1 for CRU and GPCC and to EOF4 for CCLM.
L279-280: This information should be stated in an introductory paragraph of section 3.1.
Figure 3: This figure shows precipitation anomalies, but I wonder why the anomaly is systematically computed as the departure between each season of each dataset and the CRU climatology. Why not use the climatology of each dataset?
L344-345: Already stated in the Figure caption. No need to repeat in the main text.
L351-354: The results with and without orbital forcing are not similar for temperature. I'm surprised that precipitation changes to be the same with or without orbital forcing. This may indeed be due to area-averaging or because of internal variability. I wonder if this conclusion would remain unchanged considering low-passed filtered timeseries.
Figure 7: Please modify the colors of the two curves in orange because they do not allow for a clear distinction between the two periods. Also, modify the legend to avoid confusion between the left and right ones (e.g., ERP: BCE400-362 and PI: 1800-1850).
L363-365: Most of these differences do not reach the 95% confidence level.
L373-374: During the winter, most of the NR experienced no significant changes between the two periods. Differences are very close to 0 except in western Ethiopia.
L375-376: The wetter summer conditions in the NR region during ERP could relate to a stronger inter-hemispheric gradient at this time compared to PI, as suggested by Fig. 8 (bottom panel). This would be consistent with recent studies based on global transient simulations for the last 6000 years.
L416-419: Please add references.
Figure 9: How are these patterns obtained? By reconstructing rainfall anomalies based on EOF1/2/3? Please clarify.
Citation: https://doi.org/10.5194/cp-2023-77-RC1 -
AC1: 'Reply on RC1', Mingyue Zhang, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-AC1-supplement.pdf
-
RC2: 'Comment on cp-2023-77', Anonymous Referee #2, 11 Dec 2023
Review Zhang et al., “The climate of the Eastern Mediterranean and the Nile River basin 2000 years ago using the fully forced COSMO-CLM simulation”.
General comments
The paper by Zhang et al. presents results from a transient climate simulation covering 500 BCE to 2018 CE. This simulation has been performed with the paleoclimate version of the COSMO-CLM regional climate model (RCM), which was forced with a global simulation carried out with the MPI-ESM-LR model. Such a transient simulation with an RCM is novel and provides a spatially detailed view of the climate. In this paper, the RCM is set up for a domain spanning the Nile River basin and the eastern part of the Mediterranean. The results section consists of two parts. In the first part, the model’s performance is evaluated by comparing the simulated climate for the period 1980-2018 with observations and reanalysis data for the same time frame. This comparison indicates that the model is doing a reasonable job, except for the precipitation in the Nile River domain. The second part provides a comparison of the results for two periods (the Early Roman Period (ERP, 400-362 BCE) and the preindustrial period (PI, 1800-1850 CE)), and presents also an analysis of the link between temperature and precipitation on the one hand, and the atmospheric circulation on the other hand.
Although the performed simulation is innovative and represents a technological improvement that could play an important role in studies of past climates, there are several issues with this paper that need to be resolved before it can be published. These issues are discussed in detail below, and relate to the purpose of the paper, the set-up of the simulation and the analysis of the results.
Main comments
- Purpose of the paper. It is not clear to me what the purpose of this paper really is. One goal is to provide an evaluation of the performance of the model for the present-day climate, but this evaluation is not presented as being the main aim. The results for the ERP and PI periods are compared, and it may be expected that a characterization of the ERP climate is the main objective of this paper, as the PI period is usually taken as the reference period in paleoclimate modelling. However, the introduction section of this paper does not even mention the Early Roman Period once, and thus does not explain at all why there is a focus on the ERP. My advice is therefore to streamline the introduction section and make a case why it is important to obtain a more detailed climatological understanding of the ERP. This should include an explanation of why the 400-362 BCE period is chosen. Why not another period, and why these specific 38 years? To my knowledge, there is no established definition of the Early Roman Period that falls between 400 and 362 BCE, so this requires a thorough discussion.
- Set-up of the simulation - CCLM. The results are obtained with the newly developed paleoclimate version of the CCLM model. However, it is not explained what has been modified compared to the “normal” version. More details on the model should thus be provided. What is meant by the phrase “by implementing CCLM in model version 5.0 with CLM version 16”? this phrase suggests that CCLM is a different model from CLM, but CCLM just stands for COSMO-CLM, doesn’t it? And what is actually the spatial resolution of the version of CCLM applied here?
- Set-up of the simulation – applied forcings. Several forcings are applied in the simulation, but it is not clear what the main differences between these forcings are. The authors mention orbital, solar, GHG, volcanic and land-use changes. I suggest explaining what the main differences in radiative forcings are between the main periods of interest in this paper: present period, PI and ERP. How do each of these radiative forcings change between the three periods? What forcings are most important, and how large are the differences when expressed in Wm-2? This information is important for interpreting the results and should be included.
- Analysis of the results. The differences between the ERP and PI climates should be discussed in terms of the differences in radiative forcings between the two periods (see point 3 above). In addition, it should become clear how the results for the ERP compare to proxy-based climate reconstructions for the same period. On line 497 it is noted that “comparing modeled data with proxy records is essential for a comprehensive understanding of climate variability”, but surprisingly the authors do not make such a comparison for the results presented in this paper. This is clearly a missed opportunity, and in my opinion a model-proxy comparison should be included. Moreover, the model results should also be discussed more extensively relative to previous modelling studies.
- Discussion. The present paper presents the first transient RCM simulation of the past 2500 years, so one question to answer is if there is a clear advantage of making the effort of running an RCM in transient mode. If the authors would run three snapshot RCM experiments for present-day, PI and ERP, to what extent would the results be different? In other words, what have we learned about the ERP climate from the transient simulation that could not be obtained from a snapshot experiment with mean forcings for the ERP?
Other comments
General: I suggest indenting the first line of a paragraph to improve readability.
p. 1, Title. I find including “2000 years ago” in the title misleading, as this suggests that the climate of the first century CE is studied, but this is not the case. It would be clearer to refer to the ERP. The title is also missing a verb. So, I propose revising the title, for instance: “The climate of the Eastern Mediterranean and the Nile River basin in the Early Roman Period (400-362 BCE) as simulated in a fully forced COSMO-CLM experiment”.
p. 1, line 17. “… of particular interest due to their location under the influence of major atmospheric teleconnections.” I suggest mentioning here that the region is also of interest because of its important role in human history.
p. 1, lines 29-30. Should be “sheds light on”. And “paleoclimate” is one word. What is meant by “processes of periods of interest”?
p. 1, I suggest removing the example on volcanic eruptions from the abstract as the impact of volcanic forcing is not specifically studied in this paper.
p. 2, lines 37-45. This first paragraph is not easy to read because it seems to consist of single sentences without a clear connection. For instance, what is the connection between the “significant changes” in the first sentence and the “anthropogenic climate change” in the second sentence? Moreover, what is meant by “historical context” in the third sentence in relation to this “anthropogenic climate change”? And what is the connection between “paleoclimatic data” and the “historical context”? I suggest rewriting this first paragraph and be more specific.
p. 2, line 64: Start new paragraph here that is focused on NR.
p. 3, lines 82-84. Repetition, pleas revise.
p.3, line 112. “Climate variations are also related to changes in external forcing…”. What does “also” refer to here? This is not clear. To internal climate variability?
p. 3, line 113. “The climate system is primarily driven by solar radiation, and the variations of solar irradiance can lead to changes at decadal to centennial time scales.” I agree that the sun is the main source of energy for the climate system, but the other external forcings are also important drivers of climate system variability. This also depends on the timescale involved. For example, orbital forcing is dominant at longer paleoclimate timescales. In addition, there are important internal modes of variability such as ENSO. My suggestion is to revise this part.
p. 4, line 119. “Historical studies have matched the Nile flooding with the impact of volcanic eruptions”. How was this matched? What happened to the Nile discharge following a large volcanic eruption? Please explain.
p. 4, line 121. “…effects on the global climate”. What effects are meant here? Please clarify.
p. 4, Lines 143-146. This paragraph can be removed in my opinion, because it does not really provide useful information, as the structure of the paper just follows what is common in scientific literature.
p. 5, line 161. I guess “COSMO-CCLM” should be replaced by either “CCLM” or “COSMO-CLM”?
p. 5, line 163. Are the GHG concentrations based on ice cores?
p. 8, line 248. “.. the highest significant correlation of 0.94 for winter (DJF) and summer (JJA).” Where in Table 1 do we see this 0.94 correlation for the winter season?
p. 8, lines 255-256. “For example, GPCC winter region 5 corresponds to GPCC REOF11 as the counterpart to CRU REOF5”. I do not understand how this works. How does this follow from Table 1 and/or Figure 2? Please explain.
Figures 2 and 4. The colors in these figures are difficult to see and hard to match with the colors in the legend. And I am sure it is impossible to interpret these figures for readers who are (partly) color blind, so I propose finding another solution. Especially Figure 4 is not useful, other than that it shows that CCLM is not performing well for this region.
Figures 3 and 5. What do the percentages signify?
Figure 7. The font used within the figure is too small.
Figure 8. I propose to only plot the seasonal differences where they are statistically significant, instead of using the dotted areas.
Citation: https://doi.org/10.5194/cp-2023-77-RC2 -
AC2: 'Reply on RC2', Mingyue Zhang, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-AC2-supplement.pdf
-
RC3: 'Comment on cp-2023-77', Anonymous Referee #3, 11 Dec 2023
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-RC3-supplement.pdf
-
AC3: 'Reply on RC3', Mingyue Zhang, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://cp.copernicus.org/preprints/cp-2023-77/cp-2023-77-AC3-supplement.pdf
-
AC3: 'Reply on RC3', Mingyue Zhang, 16 Feb 2024
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 459 | 162 | 50 | 671 | 51 | 58 |
- HTML: 459
- PDF: 162
- XML: 50
- Total: 671
- BibTeX: 51
- EndNote: 58
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1