the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Mar 2026
The rise of stratification – Climate-induced changes in the thermal structure of a shallow polymictic lake until 2100
Abstract. Climate change exerts a significant influence on lake ecosystems by altering stratification and thermal dynamics. These changes are commonly quantified using climate model projections to assess future conditions. However, climate model simulations generally have a daily temporal resolution, which is inadequate for resolving the fast, sub-daily processes governing shallow lakes. Consequently, long-term impacts of climate change on shallow lakes remain underrepresented in literature. This study presents a modeling workflow for simulating and quantifying future climatic conditions in shallow environments, where sub-daily resolution is necessary. The proposed workflow comprises a weather generator for the temporal downscaling meteorological forcing, as well as a physics-based one-dimensional model for simulating lake thermal dynamics. The workflow is applied to Lake Blaton, a large polymictic lake, to assess the effects of the projected climatic changes on the lake through the end of the century. Changes are analyzed as a function of time and water depth, under the RCP4.5 and RCP8.5 climate scenarios, using an ensemble of 14 climate model simulations. Our findings indicate that stratification is likely to intensify throughout the century, in a complex interplay with water depth, in which mutually enhancing effects are accompanied by progressive dampening. The number and duration of stratified events show a slight increase, which does not reflect the magnitude of the projected intensification in stratification, suggesting that wind forcing remains a dominant factor in regulating stratification dynamics. Lastly, the evaluation of lake heatwaves using a temporally varying baseline indicates no significant changes in their characteristics.
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Status: open (until 08 May 2026)
- RC1: 'Comment on egusphere-2026-708', Anonymous Referee #1, 01 Apr 2026 reply
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RC2: 'Comment on egusphere-2026-708', Anonymous Referee #2, 10 Apr 2026
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The manuscript addresses climate-driven changes in thermal stratification in shallow polymictic lakes. However, the central research questions, methodology, and conclusions lack sufficient novelty.
1) Over the past decade, numerous studies have extensively examined the response of lake thermal structure and stratification dynamics to climate change, including in shallow and polymictic systems. For example, Woolway et al. (2022) clearly demonstrated that climate warming leads to earlier onset, prolonged duration of stratification, and altered mixing processes, with important ecological consequences. More recently, Sullivan et al. (2025) further provided mechanistic insights into how climate change regulates lake thermal regimes and ecosystem functioning.
R Iestyn Woolway, Sapna Sharma, John P Smol, Lakes in Hot Water: The Impacts of a Changing Climate on Aquatic Ecosystems, BioScience, Volume 72, Issue 11, November 2022, Pages 1050–1061, https://doi.org/10.1093/biosci/biac052
Sullivan, C.J., Read, J.S. and Hansen, G.J.A. (2025), Climate-driven alterations of lake thermal regimes. Limnol Oceanogr, 70: 2348-2364. https://doi.org/10.1002/lno.70128
In shallow and polymictic systems specifically, similar questions have already been explored in depth, see (Török et al., 2025; Zhang et al., 2025). The study seems to emphasize sub-daily processes as a key innovation. However, this aspect is also not new. Previous studies have already investigated sub-daily dynamics in shallow lakes. For example, Frassl et al. (2018) examined sub-daily variability in lake temperature dynamics, and Piccioni et al. (2021) demonstrated that thermal stratification and mixing can alternate on hourly timescales.
Török S D, Torma P. Long-term changes in summer stratification of a shallow polymictic lake by climate change and anthropogenic water level regulation[J]. Science of The Total Environment, 2025, 1009: 181025.
Zhang, M., Leppäranta, M., Heikkilä, M., Weckström, K., Korhola, A., Kirchner, N., … Weckström, J. (2025). The thermal structure of small and shallow Arctic Fennoscandian lakes. Arctic, Antarctic, and Alpine Research, 57(1). https://doi.org/10.1080/15230430.2024.2433829
2) While the authors devote substantial effort to describing the weather generation and overall modeling workflow, the actual lake model setup, calibration, and validation are insufficiently documented. These aspects are critical for any numerical simulation study, yet the authors only briefly states that “model setup, including the calibration and validation procedures and their results, are provided in Török and Torma (2025, 2024).” This is not adequate. Moreover, In Section 3.1, the validation focuses primarily on the temporal downscaling procedure. However, it should also include validation of the lake model itself, particularly its ability to reproduce thermal dynamics, including both stratified and mixed conditions. Without such validation, the reliability of the projected results remains uncertain.
Furthermore, Lake Balaton is a large (surface area ~596 km²) and elongated lake, where spatial heterogeneity, especially differential heating driven by meteorological forcing, is likely to play an important role in thermal dynamics. The current modeling approach appears to neglect the spatial variability of meteorological inputs, which may limit its ability to accurately represent lake-wide processes.
In addition, although the authors considers several meteorological variables (e.g., shortwave radiation, air temperature, and relative humidity), the treatment of wind forcing is unclear and appears insufficient despite some discussion of the limitation of wind in Discussion. Wind is a primary driver of mixing and thermal structure in open-water systems, and its long-term variability is well documented. For example, surface wind speeds have increased by 10–20% in some regions (e.g., over Lake Superior; Desai et al., 2009), while decreasing by similar magnitudes elsewhere (Vautard et al., 2010). Neglecting or inadequately representing wind forcing raises concerns about the physical realism of the model and the robustness of the conclusions.
Desai A R, Austin J A, Bennington V, et al. Stronger winds over a large lake in response to weakening air-to-lake temperature gradient[J]. Nature Geoscience, 2009, 2(12): 855-858.
Vautard R, Cattiaux J, Yiou P, et al. Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness[J]. Nature geoscience, 2010, 3(11): 756-761.
3) The Results section appears to lack direct presentation of the simulated thermal structure. In particular, there is no clear visualization of depth–time thermal dynamics showing the evolution of stratification and mixing. Instead, the manuscript immediately proceeds to climate change projections and derived metrics. This makes it difficult to assess the physical realism of the model simulations. For a modeling study of this type, it is essential to first demonstrate that the model can reproduce realistic thermal behavior, including both stratified and mixed conditions, before moving on to climate-driven changes. The absence of such direct simulation outputs (e.g., temperature contour plots over depth and time) significantly limits the reader’s ability to evaluate the robustness of the results.
In addition, regarding the climate forcing, the analysis appears to focus primarily on precipitation and air temperature. It is unclear why other key meteorological drivers—particularly wind—are not explicitly considered in terms of their long-term trends. Wind forcing plays a dominant role in regulating mixing and stratification dynamics, especially in shallow polymictic systems. Neglecting its projected changes raises concerns about the completeness and physical consistency of the forcing framework.
4) The Discussion section remains largely descriptive and does not sufficiently interpret the results within a mechanistic or conceptual framework. Wind forcing is a key driver of thermal stratification and mixing dynamics, especially in shallow polymictic lakes. However, the modeling framework appears to lack an explicit treatment of wind variability, particularly its long-term trends under climate change. This omission raises significant concerns regarding the reliability of the projections, as changes in wind forcing can fundamentally alter stratification and mixing regimes. Furthermore, the discussion of ecological implications is largely generic and reflects well-established textbook knowledge (e.g., increasing temperature leads to reduced dissolved oxygen, and stronger stratification promotes hypoxia). To enhance the scientific significance of the work, the authors should provide quantitative estimates—for example, how much dissolved oxygen is expected to decline under different climate scenarios?
5) There are also many concerns about Figs.
Figure 1 is overly simplified and lacks essential information. The figure should be structured into clearly defined panels (e.g., (a), (b), and (c)), each with explicit descriptions in the caption. It is also unclear whether the single measurement site, located in the western corner of the lake, is representative of the entire Lake Balaton system. Given the elongated morphology and known spatial heterogeneity of the lake, one site may not adequately capture basin-wide thermal dynamics.
In addition, the figure lacks geographic coordinates. The colored lines should also be explicitly defined in the caption (e.g., as bathymetric contours or isobaths).
Figure 2 is overly complex and difficult to follow. The figure contains too many elements, including multiple GOTM simulations (1 h and 6 h), different meteorological datasets, and repeated bias correction steps, which together reduce clarity. In particular, the logic within the “Lake modeling” section is unclear. The relationship between the 1-hour and 6-hour simulations is not well explained, and it is not evident why both are required. The use of dashed lines further adds ambiguity, as their meaning is not clearly defined.
Moreover, the caption is overly descriptive but does not clearly communicate the purpose of the figure. It would benefit from an opening sentence that concisely states the figure’s objective, for example: “Figure 2. Schematic overview of the modeling framework used to simulate lake thermal dynamics under present and future climate conditions.”
Figure 3 provides an important validation of the weather generator; however, the comparison remains purely qualitative. The agreement between datasets is assessed visually without any quantitative metrics (e.g., RMSE, MAE, or statistical tests), which limits the strength of the validation. In addition, the use of probability density functions may not be the most clear way to present the comparison. More direct and interpretable plots (e.g., scatter plots, time series comparisons, or bias plots) could improve clarity and make differences between datasets more transparent.
Figures 5–10 appear to be repetitive and provide limited additional insight. While multiple figures are presented, they largely convey similar information (e.g., temperature trends, distributions, or stratification metrics) without offering new perspectives. For example, Figure 5 shows temperature trends, Figure 6 presents distributions, and Figures 7–9 repeat similar analyses for related variables. This redundancy reduces the overall impact of the results. The authors are encouraged to consolidate these figures and focus on presenting the most informative and non-redundant results, potentially by combining variables or highlighting key relationships rather than repeating similar plots.
Given this context, the scientific question addressed here and the some simple results are not particularly novel. Overall, the study appears to be incremental rather than providing a substantial conceptual advance, and may be more suitable for a regional or more specialized journal.
Citation: https://doi.org/10.5194/egusphere-2026-708-RC2
Data sets
FORESEE Eötvös Loránd University https://meteordata.elte.hu/FORESEE/index.html
ERA5 ECMWF https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview
Model code and software
GOTM Lars Umlauf, Hans Burchard, Karsten Bolding https://gotm.net/portfolio/
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The overall approach of this study seems to be sound, but the generation of high-resolution data, the construction of the lake thermodynamic model, and the stratification simulation lack sufficiently reliable evaluation and validation. Additionally, many key parameters and settings are not clearly clarified, and the presentation of the projected results is also relatively simplistic.
Section 2.1: The lake is divided into four distinct basins, but the basins should be presented in the figure. The lake has an average depth of 3.5 meters and is driven by meteorological factors. However, it remains unclear to what extent the lake is stratified. Are there specific observational data to support this? Since the lake is divided into four basins, one monitoring point seems a bit insufficient to be comprehensive. Additionally, what indicators are being monitored at this single monitoring point in Fig. 1? Although ECMWF and FORESEE are only used for prediction analysis, and direct comparison among multiple schemes can eliminate the influence of their inherent errors, validation against ground observation data is still necessary.
Section 2.4: Regarding the lake model, it is a one-dimensional model—can it effectively carry out thermodynamic simulations of this lake? Additionally, when setting the model parameters and boundary conditions, the authors cited many references for support, yet there is a lack of observational data to underpin the model construction process. For example, how does this one-dimensional vertical lake model account for lateral inflow and outflow? The previous text mentioned that the lake is divided into four zones. Through different water depth settings, which zone’s thermodynamic stratification does this model actually represent? For example, regarding parameters and variables, water density is related to temperature, and the calculation formula should be provided. Additionally, how is the wind speed on the lake surface considered? These key aspects are not addressed.
Section 3.1: What is being validated in the figures and tables (Fig. 3 and Table 1)—water temperature or air temperature? Additionally, for the validation results, it is recommended to present them using scatter plots or similar methods, rather than just probability density curves. Since the key to this study is the generation of high-resolution input data, the reliability of the results generated by the combination of multiple models in this part needs to be thoroughly validated.
Section 3.4: Regarding lake stratification, it seems that the paper uses the potential energy anomaly index, which is calculated based on the water temperature profile. First, the specific simulation results of the water temperature profile are not presented anywhere in the text. Second, how this index actually indicates vertical stratification in the lake is not clearly explained. Although this index may be a quantitative measure, there are many well-established indicators for lake stratification that might be more convincing than the one used in this paper, such as the Schmidt Index, Lake Number.