the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Feb 2025
Forestation tends to create favourable conditions for convective precipitation in the Mediterranean Basin
Abstract. The Mediterranean Basin is identified as a climate change hotspot and prone to future drying. Previous studies indicate that the effect of forests on precipitation remains unclear for the Mediterranean Basin specifically. Here we use a simple model to simulate the development of the atmospheric boundary layer (ABL) to determine the impact of forest on convective rainfall potential. There is convective rainfall potential when (1) the ABL reaches the lifting condensation level, and (2) there is sufficient convective available potential energy. We model the ABL development over the Mediterranean Basin covered fully with bare soil and forest to determine its land cover sensitivity. In addition, we examine the sensitivity of the ABL to variations in soil moisture for the forest scenario specifically. We identify two distinct responses to forestation in the Mediterranean Basin dependent on soil moisture content. Forestation contributes to warming and drying in relatively dry regions (low soil moisture content) and to cooling and wetting in relatively wet regions (high soil moisture content), indicating that dry gets drier and wet gets wetter. We find that both forestation and an increase in soil moisture can contribute to convective rainfall potential. In regions with a relatively high soil moisture content, forestation positively influences both the convective available potential energy, and the crossing of the ABL and lifting condensation level. The results show that forestation in the Mediterranean Basin may reduce future drying in relatively wet regions and enhance future drying in relatively dry regions.
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RC1: 'Comment on egusphere-2025-289', Anonymous Referee #1, 06 Mar 2025
This study simulates the impact of land cover change and soil moisture availability on boundary layer development in the Mediterranean Basin using the Chemistry Land-surface Atmosphere Soil Slab (CLASS) model. By comparing CAPE and ABL height across different land cover and soil moisture scenarios, the authors determine that convective rainfall potential increases when vegetation fraction is increased over wet regions and increases linearly with soil moisture content. While the results of the experiment and its design are interesting and compelling contributions to the land-atmosphere interactions literature, I have some major reservations about the framing of the study around “forestation” as a potential climate mitigation strategy. I recommend that the manuscript undergo major revisions to reevaluate and clarify its research goals and interpretations.
Framing and Language
- Motivation: The abstract motivates the study by first identifying the Mediterranean Basin as a “climate change hotspot” that may be “prone to future drying.” The authors then follow up this statement by noting that “Previous studies indicate the effect of forests on precipitation remains unclear for the Mediterranean Basin” before diving into a description of the study. The link between climate change, future drying, and vegetation-precipitation coupling is not clear at all from these two sentences. The introduction does marginally better in explaining “forestation may increase freshwater availability” and “forestation… may enhance rainfall.” What’s missing is the underlying implication that either A) we expect forestation to occur in this region or B) forestation is being considered as a climate mitigation strategy. If you decide to stick with the forestation angle, please explain and expand on this instead of leaving the readers to try to connect the dots. Assuming B based on the discussion of regreening later in the paper, how realistic is this strategy and how seriously is it being considered for the region? How confident are we that the Mediterranean will experience drying given that hydrological trends tend to experience large disagreement between models? Some important context is missing.
- Defining and Interpreting “Forestation”: It is not clear to me whether the study actually addresses the question of how forestation would affect rainfall in the region. “Forestation” and other terms like “regreening” and “restoration” that are used liberally in the paper are 1) not well defined and 2) typically imply that there is a gradual increase in vegetation cover over some timescale that takes into account the planting and growing process. Instead, what this study does is answer the question of “how convective rainfall potential would be different over the Mediterranean Basin if the region were covered in forest” by dramatically altering the land cover properties of the grid cells across different model runs (i.e., a sensitivity study). There are no dynamical considerations in the experiment setup, so I am not confident the results can be interpreted as the climate response to “an increase in forest cover,” at least not in the context of any real-world replanting strategy. In other parts of the paper, the authors describe the results using phrases like “The differences in boundary layer characteristics between the forest and bare soil scenarios show significant spatial variation,” which is much more accurate (L199). While these results certainly have important implications for the use of forestation as a climate mitigation strategy, the title and language misrepresent the study’s scope. Perhaps I am misunderstanding what “forestation” means to the authors, but I think the study would fit much better in a vegetation-precipitation/land-atmosphere coupling context rather than with the current climate change angle.
Science Clarifications
- Sampling: 10 years seems like an insufficient length of time to establish a climatology for the region (L83). The description of the sampling method in 2.4 is extremely unclear to me. What does it mean to run the model 20 times for each grid cell with two random days being sampled for each year? Is the study only simulating the atmospheric conditions during 20 random days over the 10-year time period? This, in addition to the high number of samples that had to be filtered out, is very concerning. Please also specify that CLASS is a single column/grid cell model.
- CAPE Analysis: Why was a threshold of 400 J/kg chosen for the CAPE analysis? Given that the authors calculate CAPE using the metpy cape_cin (typo in L186) function, CIN should also be included in the analysis and would provide a more robust standard for determining the likelihood of convective initiation. Recent studies (Emanuel, 2023; Zhang et al., 2023) have also shown that the development of high CIN over wet soils is essential to explaining the development of high CAPE in both models and in observations. Given that the relationship between CAPE and soil moisture is one of the study’s main results, the discussion in L364-373 could be expanded and some of that literature should be mentioned earlier in the introduction (L64).
- Spatial Correlation with Soil Moisture Regimes: Given that the interpretation relies heavily on an understanding of the different wet and dry locations in the basin, Figure A1 should be included in the main paper to be more easily accessible. The spatial variations in Figure 2 for CAPE are quite scattered compared to the more coherent patterns in BLH and LCL. Could the authors perform some sort of correlation between the ABL/CAPE changes and the soil moisture variations across the region? That is, how well do the ABL/CAPE changes map onto the soil moisture regimes? Also, given that the study is currently framed in the context of future climate change, please comment on how we expect those regimes to change in the future. If moisture decreases in the region, will these results still hold? Please also discuss how realistic an average ∆LCL of over 8km is and rescale the plot in Figure 2.
- P-E and Moisture Recycling: Given that the study’s goal is to understand “future drying” and the changes in precipitation potential are accompanied by changes in evapotranspiration, there should be some considerations of P-E in the discussion of “wet gets wetter” (Abstract).
Citation: https://doi.org/10.5194/egusphere-2025-289-RC1 -
AC1: 'Reply on RC1', Jolanda Theeuwen, 19 Mar 2025
Dear Editor, Dear Reviewer,
We sincerely appreciate the time and effort the reviewer has taken to provide us with valuable feedback. We have carefully addressed each of the reviewer’s comments in the attached document, which includes a detailed point-by-point response.
We believe that the reviewer’s suggestions will help to improve our manuscript.
On behalf of all authors,
Jolanda Theeuwen
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RC2: 'Initial review on egusphere-2025-289', Anonymous Referee #2, 08 Mar 2025
The study examines the influence of forest cover on convective precipitation potential in the Mediterranean Basin, using the CLASS model to simulate the atmospheric boundary layer (ABL) response to changes in land cover (bare soil vs. forest) and soil moisture. While the paper is well-organized and offers valuable insights into land-atmosphere interactions, there are some major reservations I have regarding the methodology, underlying assumptions, interpretation of results, and the clarity of key definitions that should be addressed and revised in the manuscript before publication.
1. Definition of key terms
- The terms "forestation," "regreening," and "land restoration" are used throughout the manuscript but lack precise definitions. Please clarify what these terms mean in the context of this paper.
- L285: The term “parcels” is introduced for the first time in the Discussion section without prior definition, though it is an important concept in the calculation of CAPE. To ensure clarity, the authors should define this term earlier in the manuscript, preferably in the Methods section, and briefly explain its relevance to the study's atmospheric processes.
- L55 and L186: Given the study’s focus on CAPE and since CAPE is a calculated quantity rather than a height like the ABL or LCL, the authors should explicitly provide the equation used for its computation in the main text. Simply referencing the MetPy Python function is insufficient, as it does not clarify the exact formulation or assumptions applied in this study. Including the full equation and associated assumptions will improve transparency and reproducibility.
2. Study setup
- L57: “When both the ABL and LCL cross and the CAPE is at least 400 Jkg-1 there is convective rainfall potential.” The phrasing "both the ABL and LCL cross" needs clarification: does this mean the ABL height exceeds the LCL? Additionally, while CAPE represents atmospheric instability, a threshold of 400 J/kg does not inherently indicate precipitation without considering other key factors such as CIN, mid-tropospheric moisture, and large-scale forcing (Emanuel, 2023). Can the authors clarify the reasoning behind this threshold and account for additional necessary conditions for convective rainfall?
- L47: “To isolate the local effects of changes in land cover on local precipitation, a different model approach is necessary.”Please clarify what is meant by "a different model approach." Specifically, elaborate on the key differences between this approach and existing methods, and justify why the proposed model is better suited for isolating local effects on precipitation.
- L83: “This simulation is done for early summer as this is the start of the dry season.” It is not clear to me why the early summer (May and June) time period is significant for study when it is stated in L93 that there is late spring and summer convective precipitation in the region. Have the authors considered expanding the period of study (which may also improve the study sampling rate)? Please elaborate on this.
- L93: “Although precipitation falls predominantly in winter, during late spring and summer there is convective precipitation in the region.” Given that the study relies on historical data from ERA5, it would be beneficial to include a figure illustrating which grid cells recorded observed precipitation and which are currently bare soil vs. forest. This would help assess the spatial distribution of convective precipitation and clarify how well the modeled convective rainfall potential corresponds to observed precipitation events given the current state of the region. This would be relevant to strengthen the study’s conclusions and recommendation on the need for forestation in different areas to “potentially enhance local rainfall through forestation”. Can the authors provide such a figure to support this statement?
- Section 2.6 Postprocessing. I am concerned about the disproportionate exclusion of dry regions compared to wet regions due to the filtering process. The bias introduced by the increased sampling size in wetter regions undermines the generalizability of the authors' conclusion that both forestation and an increase in soil moisture can contribute to convective rainfall potential. Given that nearly half of the samples—primarily from dry regions—were excluded, can the authors clarify how this bias affects their findings? Additionally, how do the authors justify applying these conclusions across the Mediterranean Basin area of study when dry regions are underrepresented in the results? (particularly for Fig. 3). Can the authors comment on this?
- Section 2.7 Validation. The authors mention some numbers to classify “short and tall vegetation cover”, but it is not clear to me how these values are retrieved or calculated. Please clarify the source and methodology used to define these classifications.
- Section 2.8 Model output interpretation, L195: “To analyze the uncertainty of the convective rainfall potential we also study the convective rainfall potential for a change in BLH, LCL and CAPE of ±10%.” The authors state that they analyze the uncertainty of convective rainfall potential by varying BLH, LCL, and CAPE by ±10%. However, it is unclear why these 10% variations are chosen and if there is any statistical significance towards the conclusion that the “inaccuracy of the exact values may be of less importance” in L344.
- It also seems to me that this uncertainty is related to the “inaccuracy of the exact values” and assesses the sensitivity of results to minor perturbations in key variables. How do the authors account for the impact of sampling bias and dataset exclusions (particularly in dry regions) on the robustness of their conclusions (particularly for Fig. 3 and 4 and associated discussion)?
- For all figures referencing the rainfall potential color scale. Please include in the caption how rainfall potential is defined. Specifically, what constitutes a grid cell to “have a convective rainfall potential” or “have no convective rainfall potential” in the sample?
3. General manuscript proofreading
- Throughout the manuscript, there are multiple instances of missing commas and periods, which affect general readability and clarity. I recommend a thorough grammatical review to improve sentence structure, punctuation, and overall flow. In particular, some sentences lack necessary commas for readability, and certain sections contain run-on sentences that would benefit from clearer punctuation. See L27, L47, L48 for some (not all) examples. Also, see L186: "cape_sin" should be corrected to the correct function name "cape_cin", and L394: “mediterranean” should be capitalized. A careful proofreading by the authors would enhance the manuscript’s clarity.
Citation: https://doi.org/10.5194/egusphere-2025-289-RC2 -
AC2: 'Reply on RC2', Jolanda Theeuwen, 19 Mar 2025
Dear Editor, dear Reviewer,
We highly appreciate the time and effort the reviewer has taken to provide us with their valuable feedback. We have carefully addressed each of the reviewer’s comments in the attached document, which includes a detailed point-by-point response.
We believe that the reviewer’s suggestions will help to improve our manuscript.
On behalf of all authors,
Jolanda Theeuwen
Data sets
Atmospheric boundary layer development over bare soil and forest in the Mediterranean Basin Jolanda Theeuwen, Sarah Warnau, Imme Benedict, Stefan Dekker, Bert Hamelers, Chiel van Heerwaarden, and Arie Staal https://doi.org/10.5281/zenodo.14716229
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