Marie-Dominique Leroux
email: mariedominique.leroux@gmail.com

François Bonnardot

Samuel Somot

Antoinette Alias

Stephason Kotomangazafy

Abdoul-Oikil Saïd

Philippe Veerabadren

Vincent Amélie

Advancing Climate Services for Vulnerable Islands in the Southwest Indian Ocean: A Combined Approach of Statistical and Dynamical Cmip6 Downscaling

Keywords: Indian Ocean, dynamical downscaling, statistical downscaling, regional climate projections, climate services, heat waves, droughts, climate change, tropical cyclones, Small Island Developing States, La Réunion, Mascarene Islands. Practical Implications

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

author of this paper. The BRIO dataset is recognized for its utility and has already been utilized in numerous impact studies in La Réunion, contributing to the understanding and mitigation of vulnerability to climate risks.

An example of impact study conducted on La Réunion

We elaborate here on one specific application, assessing anticipated vegetation changes through ombrothermal indices. Additional ongoing applications are listed in Section 0.3. According to the Worldwide Bioclimatic Classification System of Rivas-Martinez and Rivas-Sáenz (1996-2009, www.globalbioclimatics.org), the ombrothermal index should reflect the overall usable water quantity for vegetation in relation to temperature. The standard Martonne index, based on annual inputs, proves inadequeate in La Réunion due to seasonal variations (Appendix A) and cyclonic rains (partially unusable by vegetation due to runoff). In collaboration with the National Botanical Conservatory Mascarin (CBN-CPIE Mascarin), we propose to define the ombroclimate attribute from a vegetation standpoint through daily water balance diagnostics that equalize rainfall and potential evapotranspiration.

The regional climate model (CNRM-ALADIN, presented in section 3) is employed to compute evapotranspiration (ETP) using wind, precipitation, temperature, and solar radiation, corrected with observation datasets. Crop evapotranspiration (ET0) is determined with the FAO 56 Penman-Monteith equation for a grass reference surface [START_REF] Allan | Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56[END_REF]. Daily water balance data, accounting for meteorological factors influencing plant water consumption and soil recharge, is calculated in both observations and model outputs. To address cyclonic rains, the soil's easily usable reserve (EUR) is set at 60 mm, and the total usable reserve (UR) at 80 mm. The annual count of days with soil water 4

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d on the annual percentage of days with soil water saturation below 25%, as defined in Table 1.

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d content below 20 mm indicates vegetation experiencing hydric stress. The cartographic representation of water balance, derived from observations and historical model data, aligns well with vegetation expression, highlighting east-west climate disparities, cirque-specific variations, and gradients along the northern and southern coasts (Fig. 1a). The red area reasonably delineates the so-called savannah region.The red area reasonably delineates the so-called savannah region. The Southwest Highlands' humid areas (Plaine des Makes, Tévelave) are discernible. Classes for this attribute are detailed in Table 1. 6

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d 0.3. Other impact studies using the BRIO dataset 7

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Introduction

Climate change is a global challenge with far-reaching consequences, impacting both the environment and society [START_REF]Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]. Regions like the SWIO are particularly vulnerable, hosting numerous small island developing states facing climate-related challenges [START_REF] Lu | What caused the extreme indian ocean dipole event in 2019?[END_REF][START_REF] Douglass | Archaeology, environmental justice, and climate change on islands of the caribbean and southwestern indian ocean[END_REF]. This vulnerability is exacerbated by the frequent occurrence of extreme weather events, including tropical cyclones [START_REF] Leroux | A climatology of southwest indian ocean tropical systems: Their number, tracks, impacts, sizes, empirical maximum potential intensity, and intensity changes[END_REF]. The SWIO basin, spanning from 30 to 90 degrees East, experiences an average of 9.7 tropical systems annually, with 4.8 developing into tropical cyclones (TCs) that are equivalent to a hurricane or a typhoon [START_REF] Leroux | A climatology of southwest indian ocean tropical systems: Their number, tracks, impacts, sizes, empirical maximum potential intensity, and intensity changes[END_REF].

The Southwest Indian Ocean region is not immune to the impacts of climate change. Observational records from Meteo-France reveal a clear significant warming trend of +0.17 • C per decade over the past six decades on La Réunion (Fig. 2). On the Comoros Archipelago (including Mayotte), nearsurface (2 m) air temperatures have risen at a significant rate of +0.19 • C per decade. These warming trends are not strictly linear and the warming has intensified in the recent two decades (Fig. 2). Moreover, changes in precipitation patterns are evident, with a 36% decrease in annual precipitation in the southwest region of La Réunion over the past 60 years (Appendix B). On Mayotte, dry-season precipitation (May to October) has experienced a 33% decline during the same period (Appendix B).

The Indian Ocean's most rapid sea surface temperature (SST) warming since the 1950s, highlighted by the IPCC [START_REF] Fox-Kemper | Cryosphere and Sea Level Change[END_REF], raises concerns about the evolution of tropical cyclones in the SWIO basin. Over the past 41 years, there is no significant trend in the number of observed tropical systems (Appendix C). However, while ongoing reanalysis at the Regional Specialized 10

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d Meteorological Center (RSMC) of La Réunion is assessing TC intensity, [START_REF] Fitchett | Recent emergence of cat5 tropical cyclones in the south indian ocean[END_REF] already reported an increase in Category 5 cyclones. Globally, [START_REF] Kossin | e r r e v i e w e d major tropical cyclone exceedance probability over the past four decades[END_REF] noted a rise in the proportion of Category 3-5 TC instances to all Category 1-5 instances over the past four decades. Additionally, [START_REF] Kossin | The poleward migration of the location of tropical cyclone maximum intensity[END_REF] identified a poleward migration of TC maximum intensity. From reconstructed long-term proxy data and high-resolution climate simulations, [START_REF] Chand | Declining tropical cyclone frequency under global warming[END_REF] revealed a 13% reduction in global cyclone frequency between 1850-1900 and 1900-2000.

Summarizing TC projections for the late 21st century, [START_REF] Knutson | Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming[END_REF] suggests a 14% decrease in global TC frequency for a 2

P r e p r i n t n o t p e e r r e v i e w e d

TCs is also projected [START_REF] Emanuel | The dependence of hurricane intensity on climate[END_REF][START_REF] Kossin | e r r e v i e w e d major tropical cyclone exceedance probability over the past four decades[END_REF]. Medium-to high-resolution climate projections for the South Indian Ocean align with the global trend, indicating a decrease in overall TC frequency and an increase in intense TC frequency in response to global warming [START_REF] Murakami | Future Changes in Tropical Cyclone Activity Projected by the New High-Resolution MRI-AGCM[END_REF][START_REF] Walsh | Tropical cyclones and climate change[END_REF][START_REF] Cattiaux | Projected changes in the Southern Indian Ocean cyclone activity assessed from high-resolution experiments and CMIP5 models[END_REF][START_REF] Knutson | Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming[END_REF][START_REF] Thompson | Projected characteristic changes of a typical tropical cyclone under climate change in the south west indian ocean[END_REF]. For a 2 • C warmer world, [START_REF] Cattiaux | Projected changes in the Southern Indian Ocean cyclone activity assessed from high-resolution experiments and CMIP5 models[END_REF] projects a 20% decrease in Southern Indian Ocean TC frequency, with an increased maximum intensity, poleward shift of lifetime maximum intensity, and delayed cyclone season onset. Using a pseudo global warming approach,

[67] projects a 6.5% average intensity increase, a 2-degree poleward shift, and a 33.8% rise in median rainfall rates under a scenario of 1. Notably, the Dvorak scale traditionally uses 1-minute sustained winds [START_REF] Velden | The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method that Has Endured for over 30 Years[END_REF],

but in the SWIO basin, a common practice is to apply an empirical factor of 0.88 [START_REF] Wmo | Global Guide to Tropical Cyclone Forecasting[END_REF] for conversion to 10-minute sustained wind speeds. Thus, intensity thresholds in the SWIO may correspond to slightly higher TC intensities than the standard Saffir-Simpson scale.

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P r e p r i n t n o t p e e r r e v i e w e d

To assess global and regional climate models' performance in capturing the Intertropical Convergence Zone (ITCZ) and related precipitation, we employ the IMERG product [START_REF] Huffman | Integrated Multi-satellitE Retrievals for GPM (IMERG)[END_REF] at 0.1-degree resolution and ERA5 reanalyses [START_REF] Hersbach | Global reanalysis: goodbye ERA-Interim, hello ERA5[END_REF] 31], our approach aimed to maintain model diversity and the filtered projection range. We evaluated the potential range of changes in surface temperature and precipitation across the SWIO region. Screening for the CMIP6 ensemble involved considering the IPCC AR6 likely range for Equilibrium climate sensitivity (ECS) and/or Transient climate response (TCR) [START_REF] Hausfather | Climate simulations: Recognize the 'hot model'problem[END_REF]. ECS and TCR values, critical metrics for comparing model responses to greenhouse gas changes, can be found in [START_REF] Forster | The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity[END_REF][START_REF]Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF] or in [START_REF] Meehl | Context for interpreting equilibrium climate sensitivity and transient climate response from the cmip6 earth system models[END_REF] or in Appendix A of [63]. Concerns about a subset of CMIP6 models with higher climate sensitivities, potentially leading to misleading temperature projections, have been raised in previous studies [e.g., [START_REF] Scafetta | Testing the cmip6 gcm simulations versus surface temperature records from 1980-1990 to 2011-2021[END_REF][START_REF] Hausfather | Climate simulations: Recognize the 'hot model'problem[END_REF]63]. Among the 22 models used, 7 When selecting a global model for driving the regional model (CNRM-ALADIN), we excluded unrealistic and highly sensitive models for a more accurate representation of the regional climate. Subsection 4.3 offers a detailed overview of our selection process. CNRM-ESM2-1, part of the subensemble of 7 low-ECS or low-TCR GCMs, aligns with the median range of the 22 CMIP6 models in terms of temperature evolution, thereby offering a balanced and reliable choice. It also belongs to the group of models displaying the most significant reduction in future precipitation in the region. This characteristic makes it a valuable choice for addressing severe risks related to water resources. The use of a suite of CNRM models ensures consistency between the GCM and the regional climate model (RCM), sharing the same dynamical core, physical parameterizations, and forcings [START_REF] Taranu | Mechanisms behind large-scale inconsistencies between regional and global climate model-based projections over europe[END_REF][START_REF] Boé | Large discrepancies in summer climate change over europe as projected by global and regional 64[END_REF]. CNRM-ESM2-1, also assessed for its capability to represent presentday tropical climate processes in the SWIO region, was therefore retained to 18

(BCC-CSM2-MR, CAMS-CSM1-0, CNRM-ESM2-1, FGOALS-g3, MIROC6, MRI-ESM2-0, NorESM2-LM)
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d drive the regional model.

Methods

Dynamical downscaling

Dynamical downscaling from CMIP6 simulations [historical and Scenar-ioMIP, 57, 26] was employed to derive regional climate information for a vast area in the southwest Indian Ocean, covering most inhabited countries from Mozambique's coasts (33 1900). To assess regional changes, we compare CNRM-ALADIN output values within these 30-year periods to those of the baseline 1981-2010 period for CNRM-ALADIN.

Tropical system tracking and intensity correction

Tropical systems in CNRM-ALADIN are tracked every 6 hours using the algorithm introduced and detailed in [START_REF] Chauvin | Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution[END_REF], and later used in [START_REF] Daloz | Impact of the configuration of stretching and ocean-atmosphere coupling on tropical cyclone activity in the variable-resolution GCM ARPEGE[END_REF][START_REF] Chauvin | Future changes in Atlantic hurricanes with the rotated-stretched ARPEGE-Climat at very high resolution[END_REF][START_REF] Cattiaux | Projected changes in the Southern Indian Ocean cyclone activity assessed from high-resolution experiments and CMIP5 models[END_REF]. Each cyclone is represented by a series of latitude-longitude points and its intensity is defined by the maximum 10m wind speed.

Bias correction method applied on islands

A statistical correction method is crucial in climate simulations for impact studies and climate indicators, especially when climate projections show biases compared to observations. This correction aligns simulated and observed data, improving the reliability of climate assessments. The method used here corrects the simulated distribution for each variable seasonally. Bias correction was applied to both past (1981-2014) and future (2015-2100) periods using 30-year moving time-slices over 3-month windows centered on the month to correct. The Cumulative Distribution Function-transform (CDF-t) bias correction algorithm was chosen because it accounts for changes in CDF relative to the calibration period. CDF-t, initially developed for wind speed bias correction and downscaling [START_REF] Michelangeli | Probabilistic downscaling approaches: Application to wind cumulative distribution functions[END_REF], has also been applied to temperature or precipitation [START_REF] Vrac | Dynamical and statistical downscaling of the French Mediterranean climate: Uncertainty assessment[END_REF][START_REF] Lavaysse | Statistical downscaling of the French Mediterranean climate: Assessment for present and projection in an anthropogenic scenario[END_REF].

After bias correction, the final resolution depends on observational data and ranges from about 4 km (Madagascar, Mauritius) to 3 km (Réunion) for gridded data. Station-scale bias correction was applied on smaller islands like Seychelles, Comoros, Mayotte, Rodrigues, Agalega, and St Brandon.

Bias correction for humidity, wind, and solar radiation reference parameters was conducted at a 3-km resolution over Réunion Island to facilitate daily evapotranspiration calculation using the FAO-56 formula [START_REF] Allan | Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56[END_REF].

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d types and primarily operates on a larger scale, so statistical downscaling of minimum and maximum temperatures was performed using the CDF-t method without considering weather types.

Results

Evaluation of the statistical downscaling method

The statistical downscaling method is evaluated on ERA5 reanalyses for 1981-2017, divided into a training period (1981-2010) and a testing period (2011-2017). The model is calibrated during the training period using historical data, while the testing period assesses the model's performance and its ability to accurately downscale precipitation. The ERA5 data is degraded to a 1 • resolution to align with the typical resolution of CMIP6 models. The benefit of using the 5 classes of 850 hPa wind sectors (BC-WT) is compared to applying the CDF-t correction on the entire dataset (BC). Quantile-quantile

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d (QQ) plots are generated for each wind sector type and each method to assess the improvement. Specific weather situations, including southward flow (winter cold fronts or trough situations), eastward flow (trade winds), and northward flow (tropical systems) are examined. For each scenario, the observed precipitation is compared with the ERA5 bias-corrected precipitation obtained with or without using wind regimes. For instance, for southward flow, a significant enhancement of precipitation on the southern side of the island is observed when applying wind sector correction, consistent with the observed behavior (e.g., Fig. 3).

The Taylor diagram is used to evaluate and compare the performance of the models against observed data at each grid point. The two main models evaluated are ERA5 after simple bias correction (ERA5-BC) and ERA5 after bias correction using wind-based time regimes (ERA5-BC-WT). Plotted for comparison is the ERA5 analysis interpolated at 0.03 • resolution (ERA5).

For each model, the correlation and standard deviation values averaged over all grid points are represented by large squares on the diagram (Fig. 4). The uncorrected ERA5 data exhibit less variability than observed (Fig. 4a), leading to underestimation of extreme events (see also Appendix D). However, after bias correction, the standard deviations of ERA5-BC and ERA5-BC-WT become comparable to those of the observations (Fig. 4a), indicating successful introduction of variability in the models.

ERA5-BC-WT, which corrects biases based on wind sectors, shows slightly higher average performance across all grid points (Fig. 4a) and outperforms ERA5-BC on more than half of the grid points. The models' improvements are consistent and maintain their validity over the independent validation 23

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d period (Fig. 4b). In conclusion, both ERA5-BC and ERA5-BC-WT successfully correct the large biases present in the ERA5 model, but ERA5-BC-WT demonstrates slightly superior performance overall.

Examining scores in individual wind sectors also reveals insights. In northerly winds, associated with extreme cyclonic events and heavy rainfall, RMSE and MAE values are higher, both in the TRAIN and TEST dataset. The bias correction method on ERA5 struggles to capture extreme weather conditions over the hills (circus and volcano area) when winds blow 25

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d from the North. This is due to the underestimation of heavy precipitation events in the ERA5 reanalysis (Appendix D). This issue may also occur for the CMIP6 models, impacting the performance of ERA5-BC-WT. This highlights the need for an improved approach for this regime or for a better representation of extreme precipitation events in ERA5 or GCMs. However, the statistical downscaling method is robust in capturing climate change signals for the quarters AMJ, JAS, and OND and compares well with the dynamical downscaling approach (see Appendix E). Evaluating these discrepancies against ERA5 and IMERG faces challenges due to limited observational data coverage over the ocean, particularly during intense precipitation events like tropical cyclones [START_REF] Omranian | How well can global precipitation measurement (gpm) capture hurricanes? case study: Hurricane harvey[END_REF][START_REF] Yu | Performance evaluation of imerg precipitation products during typhoon lekima (2019)[END_REF][START_REF] Ayat | How do different sensors impact imerg precipitation estimates during hurricane days? Remote Sensing of Environment 259[END_REF]. ) and significant decreases in annual precipitation (Fig. 6). CMIP5 models depict less warming and a balanced precipitation trend, while CMIP6 models mainly project a decline in precipitation under this high-emission scenario (Figs. 6,7b). For other scenarios, SSP1-2.6 and SSP2-4.5, CMIP6 models show a lighter drying trend (not illustrated).

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Future precipitation trends over the SWIO from CMIP6 models

The 22 CMIP6 models were regridded to a 2.5 latitude-longitude grid.

A significant part of the southern tropics in the SWIO is projected to experience drying by the end of the century during the second semester in the SSP5-8.5 scenario (Figs. 7e, 7f), particularly in the dry trimester of July-August-September (Fig. 7e). The precipitation decline in October-November-December is associated with a delayed onset of the rainy season (Fig. 7f). The decrease in annual precipitation is more pronounced in the SSP5-8.5 scenario (Fig. 7b) compared to the SSP1-2.6 scenario (Fig. 7a, quarters not shown). Under the SSP5-8.5 scenario, over 90% of the CMIP6 models agree on an annual precipitation decline in a southern zone located east of Madagascar and along the southeast coast of Africa by the end of the century (Fig. 7b). Conversely, during the JFM trimester, an increase in precipitation is anticipated by the majority of models over part of the ITCZ, possibly linked to the strengthening of the monsoon trough (Fig. 7c). (10 4 kts 2 ) per year, but for a +2.0 • C (+4.0 • C) GWL, it shall decrease by about 4% (11%). The decline predicted for GWLs above +2.0 • C is linked to the decrease in the number of tropical systems. Our regional projections also show that the most intense cyclones may occur at more southern latitudes in the future (consistent with [START_REF] Cattiaux | Projected changes in the Southern Indian Ocean cyclone activity assessed from high-resolution experiments and CMIP5 models[END_REF]), further increasing TC risk for the Mascarene Islands.

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d 4.7. Future precipitation trends in cities of the SWIO

The dry season (June-November) in the tropical to subtropical part of the southwest Indian Ocean is projected to become significantly drier, with precipitation deficits of 10% to 40% (Fig. 10). This is due to a delayed onset of the rainy season (starting in December instead of November) and a stronger or more persistent Mascarene anticyclone, especially under the SSP5-8.5 scenario (not illustrated). This drying trend is more pronounced as global warming intensifies. The strengthening of subtropical high-pressure systems is expected to accelerate the trade winds over the Mascarene Islands, particularly during winter, when the trade winds already blow forcefully. Over La Réunion, the topography will modulate these values through acceleration effects on the northern and southern coasts of the island. In conclusion, more intense or frequent trade wind episodes can be anticipated in the future, especially in the high-emission scenario.

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d However, these projections have a high level of uncertainty, ranging from ±5% to ±10%.

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Heat waves and extreme temperatures in St Denis

CNRM-ALADIN results show more pronounced warming on the minimum temperatures (TN) along La Réunion's west coast, while the most significant rise of maximum temperatures (TX) is expected in the highlands and interior. The mean temperature increase varies across the island, gradually increasing with altitude from the coast to the center (Fig. 4 of [START_REF] Lamy | Impact of regional climate change on the mosquito vector Aedes albopictus in a tropical island environment: La Réunion[END_REF]).

Warming differences between the coast and center range from 0. Figure 12 illustrates the evolution of heatwaves in Gillot under two extreme scenarios: SSP1-2.6 (in blue) and SSP5-8.5 (in red). The average daily mean temperatures are examined to compute thresholds on a 30-year historical period centered on year 2000. For Gillot, a heatwave is detected when the average temperature exceeds 27.9 • C (99.5 th percentile of the historical distribution). The adjacent days to this peak, for which the average temperature 40

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d remains above 27.3 • C (97.5 th percentile) for at least 3 consecutive days, are included in the episode. An episode concludes when the temperature falls below 26.9 • C (95 th percentile). In Figure 12, the size of each bubble, showing a heatwave event, reflects the severity of the heatwave, which is proportional to the cumulative heat during the episode (it is computed as the integral area under the curve above the 97.5 th percentile). The horizontal position shows the duration (in days) of the episode, while the vertical position displays the intensity, representing the maximum daily mean temperature reached during the episode.

Each year won't necessarily be hotter than the previous one due to natural fluctuations (Fig. 12). However, over the long term, heatwaves will increase in duration, severity, and intensity, with daily mean temperatures reaching up to 35.5 • C in Gillot under the high-emission scenario. The increase in heatwaves will be less pronounced in the SSP1-2.6 scenario than in the SSP5-8.5 scenario, emphasizing the need for climate change mitigation. In all cases, the increase in heatwaves will be significant compared to the historical period (barely visible in Figure 12), necessitating adaptation to their impacts.

The number of days with high temperatures is expected to significantly rise (Fig. 13). During the last decade (2011-2020), the recorded maximum temperatures have already exceeded the 31 • C threshold for an average of 38 days per year in Gillot, with a peak of 103 days in 2019 (black dots in Fig. 13).

The 2019 year was notable for its elevated sea surface temperatures around La Réunion, attributed to an El Niño-like event and a positive Indian Ocean Dipole. By the end of the century, the maximum temperature will exceed 31 models with low ECS or low TCR values (solid envelopes in Fig. 13).

4.9. Future precipitation trends over La Réunion Precipitation decreases are most pronounced on the western (leeward) side of the island, while the decline is less significant along the eastern coast exposed to trade winds.

Discussion

In this article, we have described the projected changes in precipitation, temperatures, and cyclonic activity in the SWIO basin under various greenhouse gas emission scenarios and warming levels. We used a regional climate model capable of generating realistic cyclone simulations. To understand uncertainties in precipitation and temperature modeling, we analyzed 22 CMIP6 models. These models underwent statistical downscaling using weather types over La Réunion to produce data for climate services.

We have created climate datasets in a region overlooked by CORDEX domains. Our approach surpasses conventional frameworks like large CORDEX ensembles by international communities. We used an extended domain and higher resolution than CORDEX, providing a more accurate representation of tropical system climatology and precipitation across many small islands.

However, our approach has limitations. One is the use of a unique combination of regional (RCM) and global (GCM) models for high-resolution experiments. To address this, we compared the trends observed in our models

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d the 22 CMIP6 models (Fig. 6), IPSL-CM6A-LR or UKESM1-0-LL, along with ACCESS-ESM1-5, could be interesting storylines for downscaling with CNRM-ALADIN or another regional model. This would provide additional GCM-RCM combinations to sample uncertainties over the SWIO region. We are also considering implementing a statistical emulator of our regional climate model such as [START_REF] Doury | Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach[END_REF]. rately quantify heavy precipitation events. A multivariate quantile-mapping algorithm [START_REF] Cannon | Canadian large ensembles adjusted dataset version 1 (canleadv1): Multivariate bias-corrected climate model outputs for terrestrial modelling and attribution studies in north america[END_REF] or machine learning techniques could better manage these events.

Conclusions

Our study aimed to examine the impact of climate change on small SWIO islands (including SIDS) and Madagascar, and generate CMIP6-based datasets for climate services, using La Réunion as an example. We assessed climate change under three emission scenarios and various warming levels.

In situ rain gauges revealed limitations of ERA5 reanalysis and IMERG satellite data over tropical islands with complex terrain like La Réunion and Madagascar, which tend to underestimate rainfall. We used various methods to combine the CMIP6 ensemble with downscaling techniques to obtain finescale climate information over the SWIO basin:

We consolidated reference datasets for the IOC islands.

We downloaded daily datasets for temperature, precipitation, and wind at 850 hPa for 22 CMIP6 models over 1981-2100, classified them into two categories, and analyzed temperature and precipitation dispersion.

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We conducted 12-km resolution regional climate runs using CNRM-ALADIN driven by CNRM-ESM2-1 for 1981-2100 under three SSP scenarios across an extended SWIO domain encompassing all the inhabited territories of the southwest Indian Ocean.

We corrected CNRM-ALADIN biases at station points or at 3 or 4-km resolution over the SWIO islands.

We statistically downscaled 22 CMIP6 models over La Réunion to quantify uncertainties, using an innovative bias correction method based on wind sectors. This method can be extended to other islands in the region with steep orography.

Our framework, which integrates the CMIP6 ensemble with various downscaling methods, could serve as a model for other regions with limited CORDEX coverage.

The CNRM-ALADIN storyline represents a median or high-end scenario (in the low TCR GCMs subset) for temperature and exhibits relatively dry conditions. Our study reveals significant climate change impacts in the region:

Temperature Increase: Average temperatures on small islands are projected to rise by 1.1 (SSP1-2.6) to 3. Projected climate changes in the region will significantly impact ecosystems, water resources, agriculture, public health, and economic development.

These changes will render both populations and infrastructure increasingly susceptible to a range of climate-related challenges, including heatwaves, droughts, coastal flooding, and intensified cyclonic winds and waves.

Our research provides a comprehensive overview of anticipated regional

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Figure 1 :

 1 Figure 1: Ombrothermal Index computed using daily water balance from CNRM-ALADIN bias-corrected outputs for (a) the historical period 1981-2010 and for the SSP5-8.5 scenario during the time periods (b) 2041-2070 and (c) 2071-2100. The index categories are based

  Figures1b and 1cshow vegetation changes under the driest SSP5-8.5 scenario. Anticipated impacts include a reduction in very humid zones in the east, expansion of arid areas to higher altitudes (westward savannah growth), and challenges for the semi-humid Plaine des Makes zone by the end of the century (disappearance of the blue triangle).

8

 8 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d Building thermal comfort TN, TX, RH, 10-m wind, disag-

Figure 2 :

 2 Figure 2: Evolution of the observed average annual near-surface (2 m) air temperature anomaly from 1968 to 2022, relative to the homogenized 1991-2020 baseline, across La Réunion (thermal indicator based on data from 3 weather stations).

  Due to the absence of aircraft reconnaissance in the SWIO basin, forecasters at RSMC La Réunion employ the Dvorak technique [24] on satellite imagery for storm center and intensity estimation. A tropical depression is assigned a name and becomes a tropical storm (TS) when its 10-minute maximum sustained wind speed (VMAX) reaches 34 knots. Subsequent intensification stages are tropical cyclone (TC, VMAX >= 64 kt), intense tropical cyclone (ITC, VMAX >= 90 kt), and very intense tropical cyclone (VITC, VMAX > 115 kt), equivalent to Category 4 and 5 storms in the Saffir-Simpson scale.

  belong to the low ECS (1.83-3.02 • C) or low TCR (1.22-1.83 • C) GCM sub-ensemble recommended for effective use in climate change policy [63].

Figure 3 :

 3 Figure 3: Daily precipitation (mm, shaded) over La Réunion in a trough situation occurring on 1 May 2000 in (a) ERA5 degraded at 1 • resolution, (b) observation krigged at 0.03 • resolution, (c) ERA5 after simple bias correction (BC) at 0.03 • resolution and (d) ERA5 after bias correction at 0.03 • resolution using weather types (BC-WT). The arrows in (a) represent the direction and intensity of the ERA5 wind at 850 hPa. The size of the arrows is proportional to the wind intensity, with a reference arrow corresponding to a 5 m/s wind. Precipitation values are displayed on regular grid points.

Figure 4 :

 4 Figure 4: Taylor diagram for the (a) TRAIN (1981-2010) and (b) TEST (2011-2017) daily precipitation dataset at 0.03 • obtained for different models: ERA5 interpolated at 0.03 • resolution (in purple), ERA5 after simple bias correction (BC) at 0.03 • resolution (in red) and ERA5 after bias correction at 0.03 • resolution using weather types (BC-WT, in blue). Observations are in green. Dots represent the data on grid points and squares represent the mean over all grid points.

4. 2 .

 2 Evaluation of CNRM-ESM2-1 and CNRM-ALADIN in a present-day climate Both CNRM-ESM2-1 and CNRM-ALADIN models generally capture the SWIO's precipitation climatology well compared to IMERG and ERA5 reference datasets (Fig.5). They offer reasonable representations of the intertropical convergence zone and associated precipitation, but discrepancies are observed. CNRM-ALADIN, and to a lesser extent CNRM-ESM2-1, exhibit increased precipitation in the Chagos area (located near 5 • S -72 • E) -a tropical cyclogenesis area -and a slight westward ITCZ extension. Both models also show more precipitation on the east coast of Madagascar and other windward coasts (see Fig.5).

Figure 5 highlightsFigure 5 : 4 . 3 .Figure 6

 55436 Figure 5: Average annual precipitation (mm) over the SWIO for (a) IMERG, (b) ERA5, (a) CNRM-ESM2-1 and (d) CNRM-ALADIN. Annul precipitation is averaged over time periods indicated in the subtitles of each panel. Red contours indicate precipitation greater than 3000 mm.

Figure 6 :

 6 Figure 6: Anomalies in precipitation (x-axis) and temperature (y-axis) relative to the 1981-2010 reference period, expressed as percentages. Data is derived from the regional model (represented by orange triangles), a subset of 22 CMIP6 models (colored circles, with CNRM-ESM2-1 highlighted in orange) and 15 CMIP5 models (grey circles), all under the SSP5-8.5 scenario. The data, averaged within a designated box covering the Southwest Indian Ocean basin (34 • -74 • E ; 2-28 • S), spans 20-year intervals and covers time horizons from 2050 to 2090 (symbol size increases with the time horizon). Plus symbols denote a subset of 7 CMIP6 models characterized by low ECS or low TCR values.

4. 5 .

 5 Future evolution of tropical system activity CNRM-ALADIN, with a 12-km resolution, can simulate Category 4 and 5 tropical cyclones and reproduces the climatology of tropical systems better than its parent model, CNRM-ESM2-1. It produces about half the annual number of tropical systems compared to the BT dataset confined to the same geographical domain. The model has a positive bias in the intensity of tropical systems, possibly due to lack of ocean coupling or weaknesses in physical parameterizations. After intensity calibration, the model's climatology aligns more closely with the BT dataset in terms of intensity distribution with similar ratios of systems per category. 30 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d (a) SSP1-2.6 AN (b) SSP5-8.5 AN (c) SSP5-8.5 JFM (d) SSP5-8.5 AMJ (e) SSP5-8.5 JAS (f) SSP5-8.5 OND

Figure 7 :

 7 Figure 7: Precipitation Anomalies (in %, shaded) relative to the 1981-2010 reference period over the SWIO basin projected for the end of the century under various scenarios and time frames (annual or quarterly). These projections are averaged across 22 CMIP6 models. The scenarios and time periods include: (a) SSP1-2.6 for the year, (b) SSP5-8.5 for the year, and SSP5-8.5 for the quarters (c) JFM, (d) AMJ, (e) JAS, and (f) OND. Precipitations values are averaged over the 2060-2100 period. Brown shading indicates the driest areas, while blue represents the wettest. Circles mark grid points where more than 90% of the models agree on the sign of the anomaly.

31Figure 8 :

 8 Figure 8: Evolution of the probability distribution function of the maximum wind speed of tropical systems in CNRM-ALADIN averaged across the three SSP scenarios. The historical reference period is shown in black, the mid-century horizon in dashed red lines, and the end-of-century horizon in solid red lines.

4. 6 .

 6 Future temperature anomalies in countries of the SWIO Compared to the reference period 1981-2010, near-surface (2 m) air temperatures over the SWIO region are projected to increase by an average of +1.2 • C by the end of the century under the SSP1-2.6 scenario, with an un-33This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d certainty of ±0.5 • C given by the 80% envelope of the 22 CMIP6 models (Fig.9). Large land masses like Madagascar and Africa will see the greatest increases. Under the SSP5-8.5 scenario, an average increase of +4.4 • C is expected on Madagascar, while small islands will see lower yet substantial warming of +3.1 • C to +3.7 • C, with an uncertainty of ±1.0 • C. Rates of regional warming are stable under the low emission scenario (SSP1-2.6) but accelerate under the high emission scenario (SSP5-8.5).

Figure 9 :

 9 Figure 9: Near-surface (2 m) air temperature anomalies (in • C) relative to the reference period 1981-2010 derived from biascorrected CNRM-ALADIN outputs under 3 SSP scenarios, represented by different colors, for the various IOC islands. The uncertainty ranges calculated using the 80% spread of temperatures anomalies from the 22 CMIP6 models are also indicated. The time series in the bottom left corner shows the evolution of temperature anomalies in the three SSP scenarios for CNRM-ALADIN (solid lines) and for the subset of 22 CMIP6 models (80% confidence interval envelopes). The background map represents the orography in CNRM-ALADIN.

Figure 10 :

 10 Figure 10: Precipitation anomalies (in %) relative to the reference period 1981-2010 during the dry season (From June to November) derived from bias-corrected CNRM-ALADIN outputs. Displayed are the expected precipitation anomalies for various cities in IOC countries under three global warming levels, represented by different colors. The values of cumulative precipitation during the reference period (mm) are also indicated. The time series in the bottom left corner shows an evolution of wet season precipitation anomalies averaged over the SWIO basin under the three SSP scenarios for CNRM-ALADIN (solid lines) and for the subset of 22 CMIP6 models (80% confidence interval envelopes).

Figure 11 shows

 11 Figure 11 shows precipitation trends during the wet season (December-May) under three global warming levels. Most cities in the SWIO region, except Rodrigues, St Brandon, and Toliary (located southwest of Madagascar), are projected to have reduced precipitation according to CNRM-ALADIN.

Figure 11 :

 11 Figure 11: As in Fig. 10 but for the wet season (From December to May).

2 •

 2 C in the SSP1-2.6 scenario to 0.8 • C in the SSP5-8.5 scenario. It's during the peak of the hot season (JFM) that temperatures will experience the most substantial increase (not shown), with the likelihood of extreme heat episodes particularly impacting coastal areas. Gillot, situated on La Réunion's north coast near Saint Denis, may experience temperatures comparable to those in Pamandzi, Mayotte, by the end of the century under the SSP5-8.5 scenario. Historical daily mean temperatures averaged 23.6 • C in Gillot and 26.7 • C in Pamandzi over the 1985-2014 period in CNRM-ALADIN. In the SSP5-8.5 scenario, Gillot's average daily mean temperature is projected to align with Pamandzi's historical average around 2070-2090. Maximum temperatures in Gillot could potentially reach as high as 44.5 • C under this worst-case scenario.

Figure 12 :

 12 Figure 12: Heatwaves over Gillot (La Réunion) from 1981 to 2100 for two SSP scenarios computed from CNRM-ALADIN bias corrected outputs. Thresholds for heatwave identification are defined using the 1985-2014 period.

Figure 14 showsFigure 13 :

 1413 Figure14shows median projected precipitation anomalies during the dry season for the SSP1-2.6 and SSP5-8.5 scenarios in the mid and late 21st century, derived from statistically downscaling 22 CMIP6 models over La Réunion. December is included in the dry season due to future delays in the

  with other CMIP6 models to establish the relative position of our GCM-RCM storyline. Considering the temperature and precipitation variations among 44This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d

Figure 14 :

 14 Figure 14: Median (Q50) of precipitation anomalies (%) during the extended dry season (June to December) derived from 22 CMIP6 models after applying statistical downscaling using weather types (BC-WT) at a 0.03 • resolution. Displayed are the precipitation anomalies during the dry season (From June to November) in the SSP1-2.6 (row 1) and SSP5-8.5 (row 2) scenarios for the time horizons 2041-2070 (left column) and 2071-2100 (right column) relative to the 1981-2010 reference period. The color palette the magnitude of anomalies.

7 •

 7 C (SSP5-8.6) by the century's end, with Madagascar potentially experiencing up to a 4.4 • C average increase in the high-emission scenario (SSP5-8.5), relative to the reference period 1981-2010. The uncertainty ranges from ±0.5 • C (SSP1-2.6) to ±1.1 • C (SSP5-8.5). 48 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d Heat waves: The frequency of hot days is expected to rise significantly. Gillot, located near Saint Denis on La Réunion, may experience temperatures comparable to current conditions in Pamandzi, Mayotte, by the end of the century. The average annual count of days exceeding 31 • C at Gillot is projected to escalate from 13 in the model 1981-2014 period to 50 under scenario SSP1-2.6 or 180 under scenario SSP5-8.5 by the end of the century. Regional Precipitation Changes: CMIP6 models indicate a decline in annual precipitation across the southern zone east of Madagascar, with a more pronounced reduction as global warming intensifies. Over La Réunion, this reduction may average approximately -2% per degree of global warming in both dynamical and statistical downscaling approaches. Conversely, an increase in precipitation is anticipated during the first trimester of the year over the ITCZ. The regional climate simulation also shows an increase in the magnitude of wet and dry years. Dry Season Intensification: The dry season in the tropical to subtropical part of the southwest Indian Ocean is expected to become significantly drier, with deficits ranging from 10% to 40%, due to a delayed onset of the rainy season and a stronger or more persistent Mascarene anticyclone. More intense and longer drought episodes are expected over Madagascar, the Comoros Archipelago, and the Mascarene islands. In La Réunion, a more significant reduction in precipitation is anticipated on the western side (leeward), accentuating the historical east-west contrast associated with trade winds. Cyclonic Risk Increase: The frequency of tropical cyclones is expected to decrease as the climate warms, especially for weaker storms, with an average reduction of 28% across all global warming levels. However, the frequency of Category 4 and 5 storms (VMAX > 115 kts) in the SWIO region is projected to rise with ongoing warming, reaching +30% for a +2.0 • C GWL and potentially up to +40% for a +4.0 • C GWL. Simulations also indicate a rise in maximum TC intensities, by up to a factor of 1.2. The duration of tropical systems with VMAX > 34 kts is also expected to increase as global warming progresses. As a consequence, the average accumulated cyclone energy per storm is projected to rise by approximately 36% for a +2.0 • C GWL and 49% for a +4.0 • C GWL. However, the overall seasonally integrated cyclone risk across a significant portion of the SWIO basin is expected to decrease for a global warming greater than +2.0 • C, due to the decline in the number of tropical systems. Simulations also indicate a poleward shift in the latitude of TC maximum lifetime intensity with ongoing climate warming. These findings imply higher cyclonic risks for islands such as the Mascarenes or Madagascar.

  climate shifts, emphasizing the need for greenhouse gas emission mitigation 50This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d and preparedness for climate change consequences. We have demonstrated the adaptability of CMIP6-derived climate information to address challenges in small and large, mountainous island regions. Our accessible BRIO dataset contributes to numerous impact studies on La Réunion. In conclusion, this study showcases effective information distillation in an information-scarce context and amidst limited resources, serving as a potential model for other vulnerable regions with limited CORDEX coverage. Future research will study heat stress risks in humid conditions.Appendix A. Climatology of precipitation over La RéunionLa Réunion experiences highly variable precipitation, both in space and time. Rainfall can be substantial on the east coast, while the west coast may remain dry. Rainfall patterns can shift dramatically, with extended dry periods followed by heavy rainfall events. The island holds numerous global records for rainfall duration. Rainfall is lowest on the western (leeward) coast, averaging around 500 mm per year, but increases with elevation (Fig. A.15). The Takamaka region and the eastern flank of the volcano receive particularly heavy rainfall: 7 meters per year for Takamaka (7.2 m in the kriged dataset) and more than 10 m per year in the Highlands of Sainte-Rose (9615 mm in the kriged dataset), which is exceptional on a global scale (Fig. A.15). The leeward region experiences prolonged drought periods during the austral winter (over 300 days without precipitation in Le Port), whereas the windward region has better-distributed rainfall throughout the year (over 300 days of precipitation in Piton Sainte-Rose).

Figure A. 15 :

 15 Figure A.15: Average annual precipitation (mm) over La Réunion in the kriged spatial observation product at 0.03 • resolution over the 1981-2010 period.

Table 1 :

 1 Classes for ombroclimate index defined through the water balance.

Table 2 :

 2

Utilization of the BRIO dataset in select impact studies conducted in La Réunion, focusing on minimum (TN) and maximum (TX) temperatures, relative humidity (RH), precipitation (RR), winds and solar radiation. LACy, PIMIT, PVBMT, PIMENT, and EnergyLab are all laboratories affiliated with La Réunion University.

  at 0.25-degree resolution. IMERG, a NASA product, provides global precipitation estimates with approximately 10 km spatial resolution over about 20 years, merging data from microwave and infrared sensors. Monthly rain gauge measurements correct biases in satellite estimates in the used Final

run product. Although IMERG products show satisfactory performance in detecting spatial variability and a 62% accuracy rate for reconstructed precipitation in hurricane studies

[START_REF] Omranian | How well can global precipitation measurement (gpm) capture hurricanes? case study: Hurricane harvey[END_REF]

, they tend to underestimate high-intensity precipitation during tropical cyclones

[START_REF] Yu | Performance evaluation of imerg precipitation products during typhoon lekima (2019)[END_REF]

. An evaluation of precipitation climatology in IMERG and ERA5 datasets over La Réunion is detailed in Appendix D.

2.2. CMIP6 ensemble for representing modeling uncertainty

A CMIP6 ensemble [historical and ScenarioMIP, 57, 26] was chosen to represent modeling uncertainty in temperature and precipitation projections over the century in the SWIO basin. The selected ensemble comprises 22 global climate models from the CMIP6 archive (https://esgf-node.llnl. gov/search/cmip6/), providing daily data for precipitation, temperature, and wind variables for at least the two extreme scenarios (SSP1-2.6 and SSP5-8.5) at the time of the study. Unlike a performance-based model selection for specific regions [e.g, 58,

  • C at Gillot half of the year if no action is taken to reduce global greenhouse 41 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862

	P r e p r i n t n o t p e e r r e v i e w e d

  The statistical downscaling method we used, based on weather regimes, was developed for an insular tropical context like La Réunion. It can be extended to other islands in the region, particularly those with complex terrain, such as Madagascar and Mauritius, provided that the wind regimes are tailored to local climate specificities. Its implementation can offer valuable insights into local-scale climate dynamics. However, it may struggle to accu-

	This approach involves training neural networks to
	learn the downscaling function between large-scale atmospheric parameters
	and small-scale surface variables daily. If successful in our tropical region, it
	could provide a cost-effective way to generate downscaled climate projections
	from other CMIP6 models.
	Another limitation in our study is the overestimation of cyclone intensities
	in CNRM-ALADIN, potentially due to the absence of ocean feedback or to
	the convection parameterization. Using a convection-permitted model like
	CNRM-AROME [10, 48] at 2.5 km resolution could provide a more accurate
	representation of extreme events, such as tropical cyclones and heavy rainfall.
	Despite this, and after cyclone intensity bias correction, our results align
	quantitatively with other high-resolution modeling studies [e.g., 14], showing
	a decrease in tropical cyclone frequency with climate warming and confirming
	that this decreasing trend is not an artifact of low resolution [40].
	Our regional climate model generally aligns with the median of the statis-
	tically downscaled CMIP6 models over La Réunion, except during the peak
	of the wet season (JFM) when deep convection is prevalent. CNRM-ALADIN
	tends to produce more precipitation over the island during this period, pos-
	sibly for valid reasons. We have therefore chosen to avoid discussing extreme
	precipitation events.

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862

Acknowledgements

We wish to express our gratitude to the following individuals for their invaluable contributions: Marie Lacoste of the CBN-CPIE Mascarin for her insightful input in defining the ombroclimate attribute; Fabrice Chauvin for his guidance with the cyclone tracking algorithm; Meteo-France's RCM team, including Pierre Nabat, Lola Corre, and Jean-François Guérémey, for their assistance with the regional model configurations; Guillaume Jumaux for supplying historical trend datasets over La Réunion and Mayotte; Jean-François Boyer for his work in creating comprehensive maps that summarize results across various IOC countries; and Kevin Lamy for his thorough internal review of our paper. Their collective expertise greatly enriched our research.

Funding

This research was supported by AdaptAction, a program developed by the AFD Group.

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Data availability

The data presented in this study are available upon request from the corresponding author. The regional CNRM-ALADIN dataset will be made publicly available in due course at climat.coi-ioc.org.

Appendix B. Historical trends of precipitation over La Réunion and Mayotte

There hasn't been a notable historical trend in annual precipitation across La Réunion as a whole. However, over the past six decades, there has been a 36% decline in annual precipitation in the southwest region (Fig. Appendix C. Historical trends on the number of tropical systems in the SWIO While there is no significant evolution in the number of tropical systems over the southwest Indian Ocean, both at the tropical storm and tropical

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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4728862 P r e p r i n t n o t p e e r r e v i e w e d Philippe Veerabadren: Investigation, Formal analysis, Software, Data curation. Vincent Amélie: Investigation, Formal analysis, Software, Data curation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this manuscript the main author used Chat-GPT in order to improve the readability of some sentences and reduce the number of words. After using this tool, the main author reviewed and edited the content as needed and takes full responsibility for the content of the publication.

Abbreviations

The