Validation of the ALARO1-SFX (CY43T2) regional climate model over Belgium across different resolutions

Dewettinck, Wout; Van de Vyver, Hans; Degrauwe, Daan; Hamdi, Rafiq; Van Ginderachter, Michiel; Van Schaeybroeck, Bert; Van Weverberg, Kwinten; Vandelanotte, Kobe; Caluwaerts, Steven; Termonia, Piet

doi:10.5194/egusphere-2025-2043

Preprints

https://doi.org/10.5194/egusphere-2025-2043

Preprints

11 Mar 2026

| 11 Mar 2026

Validation of the ALARO1-SFX (CY43T2) regional climate model over Belgium across different resolutions

Wout Dewettinck, Hans Van de Vyver, Daan Degrauwe, Rafiq Hamdi, Michiel Van Ginderachter, Bert Van Schaeybroeck, Kwinten Van Weverberg, Kobe Vandelanotte, Steven Caluwaerts, and Piet Termonia

Abstract. Regional climate modeling is essential for providing reliable information to understand localized impacts and guide adaptation strategies in the context of climate change. This study validates long-term continuous climate simulations over Belgium performed with the ALARO1-SFX model, a novel version of ALARO-1 that incorporates an advanced surface scheme (SURFEX) and improved physiographic datasets.

A scale-selective validation setup is introduced, employing a multi-level dynamical downscaling framework to assess the progressive added value of increasing resolution from the mesoscale to convection-permitting scales. The model's performance is evaluated against a gridded observational dataset and precipitation station measurements, focusing on temperature and precipitation biases, diurnal precipitation cycles, and extreme precipitation statistics.

Results indicate that higher resolutions (12.5 km and 4 km), combined with the integration of SURFEX into ALARO1-SFX, improve temperature and precipitation biases relative to the 25 km simulation, with the 4 km resolution providing the best representation of hourly extreme precipitation and its diurnal cycle. However, all simulations exhibit varying degrees of wet and cold biases. The findings underscore the added value of convection-permitting modelling for resolving extreme precipitation events and improving diurnal precipitation cycles. Furthermore, the study provides experimental evidence that increasing model resolution adds value for simulating extreme precipitation even when employing a scale-aware deep convection parameterization.

Received: 30 Apr 2025 – Discussion started: 11 Mar 2026

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Wout Dewettinck, Hans Van de Vyver, Daan Degrauwe, Rafiq Hamdi, Michiel Van Ginderachter, Bert Van Schaeybroeck, Kwinten Van Weverberg, Kobe Vandelanotte, Steven Caluwaerts, and Piet Termonia

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-2043', Anonymous Referee #1, 24 Apr 2026
This study presents an evaluation of the ALARO-1 model coupled to SURFEX on several resolutions for Belgium, with a focus on temperature and in particular precipitation. The authors show that higher resolutions improve modelled precipitation considerably despite the presence of a scale-aware convection scheme. The manuscript is generally well written and fits the scope of the journal, but more interpretation and evaluation of several variables have to be explored before it can be presented as a full evaluation paper. The following comments should help with solving the remaining issues before publication, with e.g. L1 referring to line 1.
General comments:
In the manuscript it is described that SURFEX is introduced for the ALARO-12km and ALARO-4km simulations, while ISBA is used for ALARO-25km. SURFEX, however, is barely introduced and the chosen SURFEX settings are not explained. SURFEX has a multitude of options and parameterizations to choose from, many of them can significantly impact your results, especially temperature. To understand the biases in e.g. Figure 2 and whether they are caused by changing the resolution or by using SURFEX, it is crucial that SURFEX is properly introduced. More specifically I have several questions: how many soil layers are used, do you use ISBA-DIF, how many patches do you use for the nature tile, what do you use for runoff, root distributions, snow etc., and what do you use to calculate fluxes over water (ECUME or ECUME6 maybe)? Especially the number of soil layers can greatly impact modelled turbulence and near-surface temperature during dry months in spring or summer. See also some specific comments.

The authors have compared their results extensively with previous work, but the manuscript could benefit from more interpretation in general of the results shown in this work, especially in the discussion section. Some specific comments are related to this.

The manuscript is presented as an evaluation of ALARO-1, but more processes have to be studied to identify the strengths and weaknesses of the model before it can be presented as such. Analysis of the energy balance components (downward and upward shortwave and longwave radiative fluxes, sensible and latent heat fluxes) will provide valuable information about how well several aspects of the climate system are modelled and can be compared to in-situ observations. It could reveal for example problems during clear-sky conditions or cloudy conditions, to the albedo or turbulence, or a too dry soil during summer. It is thus imperative to understand why the temperature biases presented in Fig. 2, Fig. 3 and Fig. D1 occur and why they change with resolution. Adding a section about surface fluxes is thus desirable.

Similar as last comment, as a new surface scheme is introduced, a short section about its impact on the model should be considered. If possible, running ALARO1-25km with SURFEX could reveal much of the impact of SURFEX when comparing with ALARO1-25 km that uses only ISBA. Processes like available soil moisture and runoff and its feedback on precipitation can be explored.

The terms ‘validation’ and ‘evaluation’ are both used in the manuscript, but evaluation is the more suitable term for this study. I would recommend to use ‘evaluation’ consistently in the manuscript, including in the title.

Showing statistical significance on the bias maps (Fig. 2, 4, D1, D2) could help improve interpretation of the results.

Specific and technical comments
L13: Consider rephrasing: “Furthermore, the study provides experimental evidence…”

L46: “… associated with the low-resolution parameterization of deep convection” --> “associated with the parameterization of deep convection used in lower resolution models”, or something similar.

L57-L59: Can you be more specific what uncertainties you refer to?

L58: “… uncertainties in the short term”, can you define ‘short term’ here?

L65-L66 and elsewhere in the text: Make sure to define the abbreviations first before using the abbreviations, such as ALARO and ISBA, or ARPEGE on L112 and a few other lines.

L66: But ISBA is also part of SURFEX, can you be more specific what will be replaced or introduced by SURFEX?

L78-L81: Can you define here what you will evaluate in this study?

Merge L84-L88 with L65-L71 as this should be mentioned earlier.

L89: “… we evaluated long-term …” --> “… we evaluate long-term …”, and L93: “In contrast, this study utilized … ” --> “In contrast, this study utilizes … ”.

L95: What do you mean with: “This new version marked a step-wise change compared to the original ALARO-0”?

L111: Can you be more specific what parts of the IFS and of ARPEGE are left in ALARO?

2.1.1: Can you explain in more detail SURFEX (see general comments), but also what kind of radiative, turbulence and cloud schemes are used?

L125: Please specify what variables are forced by ERA5 at the boundaries.

L128-L129: Can you explain why you nest the 4 km run in the 12.5 km run and the 12.5 in the 25 km run; why do you not force all runs directly with ERA5?

L137 and elsewhere: Readability would improve if you would refer to the panels of the figures by e.g., Fig. 1a.

L146-L147: It makes it difficult to interpret whether the changes between the 12.5 and 25 km runs are caused by introduction of SURFEX or the resolution. Running on 25 km with SURFEX could help with this. Also more information about the chosen SURFEX is necessary (see general comments).

L149: Please add a reference to the CLIMATE-GRID data set.

L153-L154: Is precipitation measured the same way for all data sets? If not, consider adding this in Table C1.

L159: “Rainfall data from all…”, earlier you mention that the observations are precipitation, but now you mention rainfall. So are these stations only measuring rainfall then or also snow?

L173-L174: Please add a reference here.

In Sect. 2.3 – 2.4, consider using the present tense.

L183: Define that T-year and d-hourly as the return period and duration here.

L196: Can you explain here why you want to use IDF curves?

L204-L205: “Near the coast, all simulations exhibit a small warm bias”. Do you know why?

Figure 2 and all other figures: Can you add over what time period these biases are calculated and use (a), (b), etc. for the panels and use (a), (b), etc. in the caption and in the text.

L209-L215: This could be related to a different representation of the soil layers in SURFEX, but it is hard to say without more information about the SURFEX settings. SURFEX with ISBA-DIF has the tendency to dry out rather quickly in summer, raising the sensible heat flux and reducing the latent heat flux and consequently increasing the near-surface temperature, especially during dry years.

L218: “The minimal bias” --> “The largest negative bias”.

L219-L221: Just a thought, but could this be caused by accidentally using water grid points as well in the interpolation near the coast?

L222-L223: “… and seasons (Fig. D2). A notable exception is the dry bias in summer (July and August)…” --> “… and seasons (Fig. D2), except for summer (July and August)…”

L226: “ALARO-4km preforms best when evaluating the seasons separately”. Consider using the root-mean-square-error (RMSE) to quantify this.

L233-L241: Please mention the subfigures here where you refer to, which would improve readability.

Figure 6: “Intensity-Duration-Frequency (IDF) curves…”, but you do not show curves in this figure. Also mention that you use a log-log scale in the caption on both axes. Furthermore: “… stations observations or simulations…” --> “… station observations or simulations…”.

L248-L255: This part is somewhat hard to follow. Also please define R² here and notice that you do not show a fitted linear trend in the figures even though you discuss them in the text.

L259: “… was large” --> “… is large”.

Figure 7: You have defined the whiskers, but also define the confidence interval that the boxes represent. Please also add (a), (b), etc. to the panels and use them in the caption and the text.

L264: “…extremes increase…”, in intensity I suppose?

L270-L271: “Furthermore, the observational data set reveal a secondary peak in the early morning, which is absent in all simulations”. Do you have any idea why?

L281: “This difference in configuration might explain the clear distinction between these two sets of simulations” and L298: “This suggests that the use of SURFEX is responsible for the improved temperature bias”. This should be explored in more detail (see general comments). Also consider using the root-mean-square error as a metric.

L302: “These patters indicate that the inclusion of SURFEX exerts a greater influence on average precipitation than changes in spatial resolution”. Or it could be that the jump from 25 to 12 km resolution matters considerably, but it is hard to say without more analysis on the impact of SUFRFEX (see general comments).

L304: “Our results for rainfall (Fig. 4) …”, but Fig. 4 shows the precipitation according to the caption, so it is not only rainfall I suppose?

L305: “The annual bias from ALARO-25km is slightly larger than the bias over Middle Europe…”. Please quantify this by mentioning the biases from this study and the bias over Middle Europe.

L327: “ALARO-4m” --> “ALARO-4km”.

L326-L328: For winter precipitation … than those in De Troch (2016)”. Can you explain why?

L329-L336: Refer to subfigures here.

L333-L334: However, for all simulations, the diurnal peak is later than the observational peak, which is in contrast with results from De Troch (2016)”. Can you explain this?

L342-L345: Do you have an idea why this drizzle bias exists?

L352: No curves are plotted in Fig 6., but I understand what you mean.

L372-L373: “For longer durations (>24 hours), ALARO-25km tends to overestimate return levels, particularly for short return periods”. Can you refer to a figure here and can you elaborate more?

L380-L382: “This behaviour … at high resolutions”. So why does this happen then?

L388-L390: The diurnal cycle … of this work”. Do you know why your work differs to the work of Van De Vyver er al. (2021)?

L405: You could mention that the introduction of SURFEX could be important as well, as it is suggested here that only resolution is relevant.

L409-L413: The scale aware scheme 3MT should be mentioned here or elsewhere in the conclusions, as it is an important finding of this study that the precipitation biases generally decrease with increased resolution despite 3MT.

L453: Define F(x) here.
Citation: https://doi.org/10.5194/egusphere-2025-2043-RC1
RC2:
'Comment on egusphere-2025-2043', Anonymous Referee #2, 01 May 2026
Review of “Validation of the ALARO1-SFX (CY43T2) regional climate model over Belgium across different resolutions” (egusphere-2025-2043)

General comments

This study presents a multi-resolution validation of the ALARO1-SFX model over Belgium, with emphasis on temperature and precipitation biases, the diurnal cycle, and extreme precipitation statistics. The topic is relevant for GMD and the experimental design (multi-level dynamical downscaling) is sound. However, several important aspects need to be strengthened before the paper can be accepted as a full validation study.

The validation relies primarily on station-based data (gridded from stations). Satellite products such as GPM (Global Precipitation Measurement) are not used. While satellite retrievals have uncertainties, they could provide valuable information on the spatial distribution of precipitation (including extremes and diurnal cycle) and help identify the origin of model biases.

The authors aim to assess the added value of increasing resolution from the mesoscale to convection-permitting scales, partly to test the scale-aware 3MT scheme. However, the 12.5 km simulation is not truly in the “grey zone”(4-10 km) where both parameterised and explicit convection are relevant. To properly demonstrate the advantage of 3MT, a sensitivity experiment with deep convection fully turned off at 4 km (i.e., a purely explicit run) would be needed to isolate the contribution of the scale-aware scheme. Without such a comparison, the claimed added value of 3MT remains circumstantial.

The study uses the hydrostatic dynamical core for all simulations, including the 4 km convection-permitting run. At this resolution, non-hydrostatic effects (e.g., strong convective updrafts) are often non-negligible. The authors should justify why the hydrostatic assumption is still valid at 4 km, or at least discuss the potential impact of this choice on the results, especially on hourly extreme precipitation and its diurnal cycle.

The explanation for temperature biases (e.g., coastal vs. inland patterns, seasonal compensation) is rather speculative. The authors do not analyse temperature advection, surface albedo, cloud cover, or surface energy fluxes. For instance, the apparent anti-phase relationship between temperature and precipitation biases (Figs. 1D and 2D) suggests that cloud-radiation or soil-moisture feedbacks may be at play. A more process-based analysis (e.g., using radiation and turbulent flux outputs) is necessary to understand the sources of the temperature biases and why they change with resolution.

The study focuses on IDF curves and return levels, which are interesting but somewhat difficult to interpret (Fig. 6). Conventional diagnostics such as precipitation frequency (wetday frequency), mean precipitation intensity, percentiles (e.g., 99.9th), and the spatial distribution of the diurnal peak are largely missing. These metrics could clearly demonstrate the common “too-light-too-frequent”bias at low resolution and would better illustrate the added value of the 4 km simulation and the scale-aware 3MT scheme.

Specific and technical comments

L39-45 The authors mention problems when convection parameterisation is turned off in the grey zone. Please specify these problems more concretely (e.g., excessively strong or too-rare deep convection, biases in precipitation intensity and frequency, misrepresentation of the diurnal cycle). Moreover, a brief summary of existing scale-aware convection schemes and their typical behaviour would help contextualise the 3MT scheme. This paragraph might be better placed after line 56 and merged with lines 57-64.

L190-194 Dividing Belgium into three regions based on elevation is reasonable. However, it would be helpful to show the observed mean annual precipitation for each region to verify that the regions indeed have distinct precipitation climates, as assumed.

L212-221 The interpretation of temperature biases in terms of seasonal compensation and SST lag is interesting but speculative. To strengthen the analysis, please examine temperature advection, surface albedo, cloud cover (from the model output), and possibly surface energy fluxes. This would help to understand the cause of temperature biases.

L222-228 The authors discuss annual and seasonal precipitation biases, but they do not separate precipitation frequency from intensity. The “wet bias” could arise from either too frequent events, too high intensity, or both. Analysing these two components separately (e.g., using maps or domain-averaged values) would better demonstrate the added value of higher resolution.

L239-241 The statement that “ALARO-25km tends to produce too many light precipitation events” could be shown much more convincingly with simple maps of precipitation frequency and mean intensity. These would visually confirm the “raining too often with too little intensity” behaviour without resorting to the word “tends”. Similarly, the analysis of the diurnal cycle (Fig. 8) uses only station-aggregated data. The authors should consider using GPM data to examine the spatial distribution of the diurnal peak across Belgium.

Fig. 6 The IDF plots are dense and difficult to read because each panel contains many markers for different return periods and durations. Consider separating return periods (e.g., different panels) and connecting points with lines. Alternatively, using precipitation percentiles (e.g., the 99.9th percentile of each duration) might provide a simpler and more direct comparison among the simulations.

L270-271 The secondary morning peak in the observed diurnal cycle is very interesting. However, since the figure shows an ensemble over all stations, it may mask regional differences (some stations may have a morning peak, others only an afternoon peak). The authors are encouraged to use GPM or a high-resolution gridded product to map the spatial distribution of the diurnal peak timing. This could reveal whether the model’s failure to capture the morning peak is due to poor performance in specific areas.

L298 The statement “This suggests that the use of SURFEX is responsible for the improved temperature bias” is too strong without process-based evidence. Please analyse temperature advection, surface albedo, cloud cover, and/or surface fluxes to support this attribution. Currently, it is not clear whether the improvement comes from SURFEX itself or from the higher resolution (or both).

L339-340 “This implies that the precipitation frequency is overestimated.” This claim can be easily verified with a spatial map of precipitation frequency (e.g., number of wet hours per year or season). Please add such a figure to directly support the statement.

L349-350 The statement that the later diurnal peak at lower resolutions “may be attributable to the scale-aware character of the 3MT scheme” is plausible, but the difference could also be caused by the land-surface scheme (ISBA vs. SURFEX) or by other aspects of the physics. A more cautious wording or a short sensitivity test (e.g., running ALARO-12km with ISBA) would help to isolate the cause.
Citation: https://doi.org/10.5194/egusphere-2025-2043-RC2

Wout Dewettinck, Hans Van de Vyver, Daan Degrauwe, Rafiq Hamdi, Michiel Van Ginderachter, Bert Van Schaeybroeck, Kwinten Van Weverberg, Kobe Vandelanotte, Steven Caluwaerts, and Piet Termonia

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Short summary

This study assesses an updated version of the ALARO regional climate model over Belgium at multiple resolutions, by using long-term climate simulations. Incorporating the land surface model SURFEX and simulating at higher resolutions led to improved simulation of temperature, precipitation, and extreme rainfall events. These findings support the value of high-resolution modelling for better representing local climate extremes and informing adaptation measures.


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