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
Mingyue Zhang
Eva Hartmann
Sebastian Wagner
Muralidhar Adakudlu
Niklas Luther
Christos Zerefos
Elena Xoplaki
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.
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Mingyue Zhang et al.
Status: open (until 19 Dec 2023)
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RC1: 'Comment on cp-2023-77', Anonymous Referee #1, 21 Nov 2023
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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
Mingyue Zhang et al.
Mingyue Zhang et al.
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