the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Apr 2025
Characteristics of Agricultural Droughts in CMIP6 Historical Simulations and Future Projections
Abstract. This study explores changes in agricultural drought event characteristics in projections of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for different future scenarios based on three Shared Socioeconomic Pathways (SSP). To quantify the intensity of agricultural droughts, the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6) with a 65 year reference period is applied to the simulations of 18 ESMs.
In a first step, these ESMs are evaluated based on performance metrics and pattern correlations of drought related variables including precipitation and approximated reference evapotranspiration with reanalysis datasets including ERA5 and CRU. With this we extend the model benchmarking performed in the third chapter of the IPCC AR6 by 15 years and additional variables.
In a second step we analyze global and regional projected SPEI6 distributions to estimate and characterize the changes in agricultural drought in the future based on multi-model means of change rates, distributions and relative area covered by specific events. We quantify the change of drought index values for 42 IPCC AR6 WG1 reference regions individually with a focus on those with most harvest area and find negative trends in water budget and SPEI for higher emission scenarios in most of them, particularly in the Mediterranean and other arid regions. This agrees with other recent studies. Increasing reference evapotranspiration emerges as the dominant driver for drier conditions in these regions. What is considered as the driest 2.3 % months during 1950–2014 is projected to be the new normal or moderate condition in arid regions by 2100, following a high emission future scenario (SSP5-8.5). For this scenario, 20 % of the harvest regions surface is considered to be under extreme drought conditions during northern hemisphere autumn. Under a low emission scenario (SSP1-2.6) with an expected global warming of 1.8 °C it would be less than 10 %. Our results show a significant difference between future scenarios regarding distribution shifts and spatial extend of extreme drought conditions in harvesting regions and can serves as a foundation for further impact and mitigation studies.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-1517', Anonymous Referee #1, 11 Jul 2025
This study is highly relevant to the scope of the journal and well-written, making it easy to read. The analysis is comprehensive, and the datasets and methodologies used are appropriate, including innovative approaches. Additionally, I particularly appreciated the high-quality graphics, which enhance the manuscript's clarity and visual appeal. My main concerns involve the lack of detailed information in certain parts of the methodology. Overall, I recommend a minor revision to address these clarifications and provide additional information.
Main points:
- The objectives and added value of the study could have been better framed in the introduction.
- L154-156: Some additional information on this approximation is needed here.
- L179-180: Please provide some information on the methods used by the CRU for the calculation of Eto. If I am not mistaken, they used the Penman-Monteith method. This is just to avoid giving the wrong impression that this is a measured parameter.
- A sub-section on the methods used to evaluate the CMIP6 models is missing (e.g., pattern correlations, inter-model comparison using root mean squared distance, etc.). Some of this information is provided in the results (e.g., Fig. 1. Caption); however, I recommend providing this information in a brief sub-section of Methods.
- The same applies to the harvest areas. The first information is provided in L255. These need to be specified in the Methods/Data sections, while some context should also be provided in the Introduction.
Minor points:
- L123: The numbering of figures should be consistent with their appearance in the text For example, Fig.6 should be Fig. 1 or just skip the reference to this figure, since you already provide another reference.
- L145-150: Please just mention if these projected warming levels are with respect to the preindustrial period.
- Table 3: Given that the reanalysis/observational datasets are described in the text, I believe this table is redundant and can be removed.
- Figure 5: Please provide the scenarios in the three panels (I assume it is SSP1, SSP2, SSP5 from left to right?) This Figure could also merge with Figure 4.
- Figure 6: Since these are not results generated in the present analysis, I would move this earlier in the text, where harvest areas and IPCC regions are first mentioned.
- Figure 7: Please provide units in the y-axis of panel (c). The same applies to panel (b), which I assume is the SPEI value per decade?
Citation: https://doi.org/10.5194/egusphere-2025-1517-RC1 -
RC2: 'Comment on egusphere-2025-1517', Anonymous Referee #2, 12 Aug 2025
This study uses projections from Earth System Models belonging to the CMIP6 protocol to assess changes in agricultural drought characteristics under three future scenarios, using the SPEI6 drought index and with a specific focus on global harvest areas.
The topic is highly relevant to understanding the evolution of droughts under climate change and could support adaptation strategies, particularly for regions with significant agricultural importance. The paper is well written, the figures are of high quality, and the results are very relevant to climate change studies. I recommend this paper for publication.
Despite this, I have a few minor comments that I would like to see addressed. Overall, I believe there is a lack of transparency in the description of the methodology used for evaluating the models. Here are my specific comments:
Specific Comments
- I found the evaluation of historical simulations to be quite weak. First, the methodology section lacks a clear explanation of how the evaluation was implemented and an explicit definition of all the metrics used, making it difficult to interpret the results, which are also vague. For example, you introduce the variable P in line 195, but you never explicitly state what it refers to. I assume it is the Pearson correlation computed over all grid points (averaged over time), but this is not obvious. Moreover, if I understand correctly, you are only evaluating the models' performance on the time-averaged spatial pattern. I think an additional evaluation of the seasonal cycle and trends would also be important.
- In Figure 1, in panels a and b, you use different names for the same variables. Please make them consistent.
- Line 204: Please explicitly state how the RMSD was calculated.
- You do not state the rules for determining whether a model is kept or disregarded based on its performance in historical simulations.
- Lines 291-292: Is the fraction area calculated with respect to the entire non-glaciated region, following Equation 4? Please be more explicit.
- Line 294: Please correct the typo: spacial into spatial
- Lines 295-296: This sentence is unclear to me. Please clarify what you mean here.
- Lines 303-304: Is this a model-related effect, or do you have another explanation for this? For example, could it be due to increased variability leading to more extreme conditions (both wet and dry)?
- Line 360: You state that you find similar global trends, but I don't understand where this comment comes from. It seems to me that you are only evaluating the time-averaged spatial patterns. I suspect this is another consequence of the lack of transparency in the methodology and results.
- Line 370: Please correct in this scenarios with in these scenarios
Citation: https://doi.org/10.5194/egusphere-2025-1517-RC2
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