William Dulac
email: william.dulac@meteo.fr

Julien Cattiaux

Fabrice Chauvin

Stella Bourdin

Sébastien Fromang

Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker

Keywords: Reanalysis, TC Tracking, Internal structure, Life cycle, Composites

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

Introduction

Atmospheric reanalyses have been developed continuously since [START_REF] Kalnay | The NCEP/NCAR 40-Year Reanalysis Project[END_REF] by a multitude of meteorological institutes throughout the world. While they have benefited from numerous technical improvements since then, the operating principle has remained unchanged: By combining past short-range weather forecasts with observations through an unchanging data assimilation scheme over the period being analyzed, reanalyses offer a physically and dynamically consistent global estimate of the state of the atmosphere at each time step. Such a product is of great value for the study of the atmospheric and climate system as it allows for direct comparison with the outputs from climate models. In fact, reanalyses are often used as reference for model evaluation, in conjunction with direct observations [START_REF] Cesana | Multimodel evaluation of cloud phase transition using satellite and reanalysis data[END_REF][START_REF] Voldoire | Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1[END_REF][START_REF] Fasullo | Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate Model Assessment Tool (CMATv1)[END_REF]. Because of the good spatial and temporal homogeneity they offer, reanalysis products are particularly useful for the study of tropical cyclones (TCs) at climate scales and can be used to circumvent the shortcomings of best-track catalogs [START_REF] Schreck | The Impact of Best Track Discrepancies on Global Tropical Cyclone Climatologies using IBTrACS[END_REF] caused in particular by differences in operational methodology among reporting agencies. But the major strength of atmospheric reanalysis for the study of TCs is that they offer the only mean of analyzing the internal 3D structure of present day TCs as well as the large scale environment around them. This is of utmost importance for the study of TCs in climate simulations. Reanalysis are therefore used to bridge the gap between observations and simulations for validating simulated TCs [START_REF] Murakami | Effect of Model Resolution on Tropical Cyclone Climate Projections[END_REF][START_REF] Bell | Response of Tropical Cyclones to Idealized Climate Change Experiments in a Global High-Resolution Coupled General Circulation Model[END_REF][START_REF] Rathmann | Tropical cyclones in enhanced resolution CMIP5 experiments[END_REF]. For that reason, the question of TC representation in reanalysis datasets if of great importance.

Yet, TCs in reanalysis suffer from coarse resolutions [START_REF] Walsh | Objectively Determined Resolution-Dependent Threshold Criteria for the Detection of Tropical Cyclones in Climate Models and Reanalyses[END_REF] as well as from a lack of in-situ observations in certain areas. Consequently, some reanalyses have implemented TC-specific assimilation techniques to improve TC representation. Both the version 2 of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2, [START_REF] Gelaro | The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)[END_REF] and the Climate Forecast System Reanalysis (CFSR, [START_REF] Saha | The NCEP Climate Forecast System Version 2[END_REF]) make use of a vortex relocation technique which consists in moving the position of the vortex closer to its best-track location or artificially inserting a vortex into the model's first-guess if absent or too weak. Reanalyses produced by the Japanese Meteorological Agency -namely the Japanese 25-year and 55-year Reanalyses (JRA-25, [START_REF] Onogi | The JRA-25 Reanalysis[END_REF][START_REF] Ebita | The Japanese 55-year Reanalysis "JRA-55[END_REF], JRA-55) -retrieve wind profile data from best-track catalogs and assimilate them as dropwindsonde data [START_REF] Hatsushika | Impact of Wind Profile Retrievals on the Analysis of Tropical Cyclones in the JRA-25 Reanalysis[END_REF]. Both these techniques have been shown to be beneficial to the placement of TC in reanalyses and for TC intensity [START_REF] Schenkel | An Examination of Tropical Cyclone Position, Intensity, and Intensity Life Cycle within Atmospheric Reanalysis Datasets[END_REF][START_REF] Murakami | Tropical cyclones in reanalysis data sets[END_REF][START_REF] Hodges | How Well Are Tropical Cyclones Represented in Reanalysis Datasets[END_REF].

ERA5 [START_REF] Hersbach | The ERA5 global reanalysis[END_REF] is the fifth and latest atmospheric reanalysis from the European Center for Medium-Range Weather Forecasts (ECMWF), succeeding to ERA-Interim (Dee et al, 2011) and ERA-40 [START_REF] Uppala | The ERA-40 re-analysis[END_REF].

Currently extending from 1959 to the present day (still ongoing, and is planned to extend back to 1940), ERA5 uses a wide range of observations from satellite data to ground stations, instrumented buoys and reconnaissance aircraft data through the 4DVAR data assimilation scheme and produces hourly fields with a 0.28125°horizontal resolution and 137 vertical levels up to 0.01 hPa.

The 4DVAR data assimilation scheme has been shown to perform better than 3DVAR in areas where observations are sparse (Whitaker et al, 2009) and to have better predictive skill for TCs [START_REF] Tiwari | Predictive skill comparative assessment of WRF 4DVar and 3DVar data assimilation: An Indian Ocean tropical cyclone case study[END_REF]. Lastly, The high resolution in ERA5 is also believed to improve TC representation compared to ERA-Interim [START_REF] Hersbach | The ERA5 global reanalysis[END_REF]. Additionally, the ERA5 back extension (BE) -covering the 1959 -1978 period -assimilates IBTrACS 6-hourly pressure reports to improve TC intensity [START_REF] Bell | The ERA5 global reanalysis: Preliminary extension to 1950[END_REF]. However, in a preliminary version of ERA5 BE, this unexpectedly resulted in some systems being too intense, firstly because too much weight were inadvertently given to these observations during the production of ERA5 BE, and secondly because the assimilation system would sometimes move the pressure minimum away from the observation, resulting in a minimum deeper than the reported one. The issue has been addressed in the final revision of ERA5 BE. IBTrACS pressure reports were nevertheless not assimilated for the main product that starts from 1979 onwards, but the emergence of new sources of observations at that time (in-situ and satellites) is believed to compensate for this.

In this paper, we apply the CNRM TC Tracking Scheme [START_REF] Chauvin | Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution[END_REF] to ERA5 to study TC representation in the reanalysis. First, we conduct a sensitivity analysis of our tracking scheme to its various detection thresholds by pairing detected tracks from the ERA5 to the IBTrACS historical besttrack catalog in order to maximize the probability of detection while limiting the false alarm rate. With our tuned tracking scheme, we then focus on TCs that were successfully matched with IBTrACS and look at several of their characteristics including the wind-pressure relationship, comparative life cycles as well as composited internal structure. Section 2 describes the processing done on both ERA5 and IBTrACS, the TC tracking scheme as well as the track pairing algorithm. Section 3.1 presents the performances of the tuned tracking scheme over ERA5 while Sections 3.2, 3.3 and 3.4 present different aspects of the TC representation in the reanalysis. The tracking scheme's sensitivity analysis is presented in Appendix A. Lastly, Section 4 discusses the results and provides concluding thoughts.

Data and Methods

Datasets

We use the ERA5 reanalysis from ECMWF with its native horizontal grid resolution of 0.28125°and interpolated to 0.25°. The CNRM tracking algorithm [START_REF] Chauvin | Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution[END_REF] is applied on ERA5 6-hourly fields to produce TC track data containing longitude/latitude coordinates, sea-level pressure, vorticity and maximum wind speed. NOAA's International Best Track Archive for Climate Stewardship (IBTrACS, [START_REF] Knapp | The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data[END_REF]) was then used as the reference dataset. IBTrACS merges storm track data from agencies all around the world and as such it requires careful processing, particularly for wind and sea-level pressure data. Here, we use the same IBTrACS version (v04) and post-processing procedure for IBTrACS data as in [START_REF] Bourdin | Intercomparison of four algorithms for detecting tropical cyclones using ERA5[END_REF]. In particular:

• we retrieve surface wind and sea-level pressure data by prioritizing, for each basin, data provided by the WMO agency responsible for that location. For WMO values coming from a U.S agency, we convert them to 10-minute winds using a 1.12 correction factor. If the responsible WMO agency is lacking wind data, we attempt the following:

-we average the data available among the following centers: Tokyo, Réunion, BoM, Nadi and Wellington; -if still missing, we use the wind data from USA agencies using a 1.12 correction factor to convert them from 1-minute to 10-minutes winds; Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker -if still missing, we use values from the Chinese Meteorological Administration (CMA) values using a 1.08 correction factor to convert values to 10-minutes winds; -The remaining missing values are classified as such.

• we remove tracks referenced as 'spurs' in the metadata;

• we only keep 6-hourly data (rather than 3-hourly) and remove tracks shorter than 24 hours;

• we only retain cyclonic seasons from 1981 to 2019 in both the northern and southern hemispheres (39 seasons).

Recovering missing SLP values is done similarly as for wind data, except no conversion is needed and therefore we average the available data from Tokyo, Réunion, BoM, Nadi, Wellington, USA and CMA.

Tracking Scheme

We use the CNRM Tropical Cyclone Tracking Scheme [START_REF] Chauvin | Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution[END_REF] to detect and track tropical cyclones in the ERA5 reanalysis. The tracking is done separately for each hemisphere up to 60°latitude and operates in three steps which are described below.

Detection

Detection of candidate points is first based on a relative 850 hPa vorticity threshold (VOR). A local sea-level pressure minimum is then sought within a 10°× 10°box around the candidate which is then considered to be the system's center.

We then compute the characteristic size of the system based on either the sea-level pressure gradient maxima or the radius of maximal tangential wind -whichever one is the smallest -which we call the Reference Disk Radius (RDR). This allows us to separate the presumed cyclone from its environment, which is defined as a disk with a radius of three times the RDR, delimiting a surface that is nine times larger than the TC itself. Separating the presumed TC from its environment allows to check for temperature anomaly which is calculated as the difference between the temperature inside the circle delimited by the RDR and the temperature inside the environment (RDR excluded).

More specifically, the detection process tests for the following:

• Temperature anomaly: The sum of temperature anomalies at 700, 500 and 300 hPa is to be greater than a threshold hereafter noted TANOM;

• Surface wind speed: the 10 meter maximum surface wind speed within the RDR is to be greater than a threshold hereafter noted RES;

• Warm upper core: the 300-850 hPa temperature anomaly gradient is to be greater than a threshold hereafter noted PT. This criterion defines to what extent the core should be warmer in the upper levels than in lower levels;

• Stronger wind in lower levels: the 300-850 hPa mean wind speed gradient within the RDR is to be greather than a threshold hereafter noted PW.

This criterion defines to what extent the wind speed in upper levels should be weaker than that in lower levels, as a result of the thermal wind balance applied to the warm core.

For the present study, we used VOR = 15 • 10 -5 s -1 ; TANOM = 1 K; RES = 5 m.s -1 ; PT = -1 K and PW = 5 m.s -1 . These values were selected based on the results of the sensitivity study presented in Appendix A

Linking

Linking points together is the process of joining the individual points resulting from the detection process into tracks. In the CNRM TC Tracking Scheme, this is achieved by an iterative process that selects the candidates that maximize the smoothness of the overall trajectory. Originally based on the methodology from [START_REF] Hodges | A General Method for Tracking Analysis and Its Application to Meteorological Data[END_REF], our tracking scheme maximizes smoothness by combining three quality parameters that each minimize a distance measurement while also factoring the maximum distance a TC is capable of covering in a single time step.

Knowing the position at time t, we select the next position from the candidates at time t + 1 as follows: The first parameter measures the euclidean distance between the position at time t and the candidate at t + 1. The second parameter measures the euclidean distance from the forward extrapolated position from the track at t -1 to the candidate at t + 1. Finally, the third parameter measures the distance from the backward extrapolated position from the track at t + 2 to the position at time t. By iteratively evaluating the total quality index for each candidate and at each time step, we ensure that the resulting track shows the least possible amount of inconsistencies, such as sharp turns and fragmented patterns.

Relaxation

In order to avoid the issue where a single TC track might be split into several parts due to one of the parameter falling below its threshold -thereby leading to an incorrect TC count -the tracking scheme performs what is called the relaxation process.

During relaxation, all detection thresholds are lifted and the tracking scheme completes tracks both forward and backward by searching for a vorticity maxima within a 500 km radius disk centered around the advected position of the system computed from the two previous time steps. This process goes on in both directions until the vorticity maxima falls below a newly defined threshold, referred to as the relaxation level. Because several detected tracks can -prior to the relaxation process -belong to the same TC, relaxing all detected tracks can result in overlapping duplicate trajectories. For that reason, a final step in which such duplicates are identified and removed is applied. This is done by eliminating relaxed tracks that have more than five steps in common with an already existing track. Throughout this study, the relaxation level is set at 25 •10 -5 s -1 . While the relaxation level is also a tunable threshold, it does not affect the number of detected TCs like the parameters described in 2.2 do but rather the length of the detected tracks.

Post-processing

Post-processing techniques can be used to retrospectively filter out extratropical cyclones and other mid-latitude lows that might have passed the detection tests. In this study, we use the thermal wind warm core diagnosis developed in Hart (2003) and tested in [START_REF] Bourdin | Intercomparison of four algorithms for detecting tropical cyclones using ERA5[END_REF], hereafter referred to as the VTU. This method uses the V T U parameter from Hart's phase spacedefined as the vertical derivative of the isobaric height perturbation within the 600-300 hPa tropospheric layer -to diagnose the presence of a warm upper core. Using the implementation from [START_REF] Bourdin | Intercomparison of four algorithms for detecting tropical cyclones using ERA5[END_REF], the V T U parameter is computed as follow:

V T U = P mid ∆Z| 600 hPa -∆Z| 300 hPa ∆P
where ∆Z| p is the maximum isobaric height perturbation within a 500 km radius at the pressure level p, and P mid = (600 + 300)/2 = 450 hPa. The V T U parameter is computed for each point of the relaxed tracks. A given track is removed from the dataset if it presents less than six time steps where V T U is strictly positive, or if not all time steps verify V T U > 0 for tracks that last less than 36 hours. We stress that the VTU post-processing is not used in the sensitivity study presented in Appendix A.

Track pairing method

We use a track pairing algorithm to pair detected tracks from ERA5 with those from the IBTrACS observational dataset. This allows us not only to provide detailed analysis of the performances of our tracking scheme over ERA5, but it also allows us to better evaluate TCs representation in the reanalysis as we can focus on tropical cyclones that have been successfully matched with IBTrACS, thereby eliminating possible biases that could come from spurious detections, i.e systems incorrectly classified as tropical cyclones.

The track pairing algorithm works by searching for tracks in IBTrACS that match both spatially and temporally to the detected systems in the reanalysis.

More specifically, the algorithm starts by assigning each detected track a geographic basin. and IBTrACS for that time step is less than 300 km. Thus for each IBTrACS candidate we compute the coverage ratio defined as the amount of detected time steps located less than 300 km from the candidate divided by the candidate's length. The candidate with the highest coverage ratio is selected and the detected track is considered successfully paired with a track from IBTrACS.

Note that in these conditions, a single time step satisfying the distance threshold can be enough to consider that the entirety of the ERA5 track is indeed found in IBTrACS. However, as it is highly improbable that two TC systems evolve at the same time at less than 300 km from each other, we assume that if a 'match' is found for one time step, then it should correspond to the same TC. This process produces basin specific data that may then be aggregated with other regions to produce pseudo-global information. We point out that we systematically exclude the North Indian region from our analyses due to reliability issues within IBTrACS in this region, as well as the South Atlantic basin due to the general lack of cyclonic activity.

With the resulting data, we can evaluate the performance of the tracking scheme by computing both the Probability of Detection (POD) and the False Alarm Rate (FAR), defined as follow:

POD = N Hit N IBTrACS FAR = N ERA5 -N Hit N ERA5
Where N IBTrACS is the number of reported tracks in IBTrACS, N ERA5 is the number of detected tracks in ERA5 and N Hit is the number of detected tracks that are paired with IBTrACS.

3 Results

Detection score

In this section we focus on characterizing the performances of our TC tracking scheme applied to ERA5 over the 1981 to 2019 cyclonic season range. The tracking scheme was run with the detection thresholds from Section 2.2, which were obtained from the tuning experiment described in Appendix A and was done for each hemisphere separately from 0°to 60°. The VTU method from Section 2.2 was also used to remove extra-tropical tracks and hence further reduce the FAR. Figure 1 presents the integrated FAR and POD over the 1981 -2019 period while Table 1 summarizes the yearly FAR and POD time series over this period. In contrast, the FAR is fairly consistent between basins except for the SPac which presents the highest FAR with 42 % while the WPac shows the lowest FAR with 14 %, in addition to having the best POD. Averaging over the five regions, we get a 67 % POD and 24 % FAR. The slight difference in globally averaged FAR we note compared to what [START_REF] Bourdin | Intercomparison of four algorithms for detecting tropical cyclones using ERA5[END_REF] found when using the VTU filter on the CNRM Tracking Scheme can probably be explained in large part by the differences in how the VTU post-processing is applied, and also to the fact that we do not include here the North Indian region in the analysis.

However, these integrated values hide large inter-annual variations as shown by Table 1. Most notably, the SPac basin experiences the strongest variations in both the FAR and POD with a standard deviation of 18.7 % and 21.2 % respectively and also has the highest 95 th FAR percentile with 66.6 %. Along with the SInd basin, the SPac also presents a 95 th POD percentile of 100 %.

Conversely, WPac is the most consistent basin with a standard deviation below 10 % for both metrics, the lowest 95 th FAR percentile and highest 5 th POD percentile.

Figure 2 presents the probability density map for a detected track for both a successfully matched track and one flagged as false alarm, after application of the VTU filter. In the EPac and the SInd basins, the region of activity are the same for both groups of tracks. On the other hand in the WPac, SPac and the NAtl basins, false alarms are seen to evolve predominantly at higher latitudes. False alarms tracks are usually shorter lived and marginally weaker than their counterparts but still pass the detection tests, making it difficult to filter them out dynamically during the tracking process. This is especially true given the results from A, as increasing detection thresholds has the side-effect of strongly penalizing the POD. While some amount of tropical false alarms is likely unavoidable when comparing the output of an objective tracking scheme with a reference dataset that has an inherent part of subjectivity, the false alarm density probability above 45°in Figure 2 shows that the VTU post-processing fails to remove all mid-latitudes systems. This can be explained by considering two factors: On one hand, the V T U parameter is estimated here using only two pressure level within the considered tropospheric layer. It is therefore possible that computing the parameter by fitting the vertical profile of ∆Z to the log of the pressure levels within the layer instead -as suggested by Hart ( 2003) -would yield a more accurate estimation of the parameter's sign. Secondly, because the VTU method negatively affects the POD in a non-negligible way, the filtering rule needs to express a balance between its efficiency in removing false alarms and the cost in POD. Increasing the required amount of time steps where V T U > 0 holds ultimately reaches a point of diminishing returns where the loss in POD is too great compared to the improvement in filtering efficiency.

TC intensity

We focus in this section on how TCs are represented physically and dynamically in ERA5. For this purpose, we filter out detected tracks flagged as false alarms to only keep the ones successfully matched to IBTrACS. However, the marginal densities on Figure 3 show that the SLP distribution in ERA5 is better represented than the wind speed and can reach reach category 4 on the revised SSHPS from Klotzbach et al (2020) (99.8 th percentile)

and even reaches category 5 with an absolute minimum of 923 hPa. This difference in how well both variables are represented results in a WPR that is The opposite effect also occurs but is much rarer. The confusion matrix is indeed non-triangular and shows 3 % of track pairs as being more intense in ERA5 than in IBTrACS. This effect is the most notable for IBTrACS Tropical Storm (TS) category for which this number reaches 6.4 % and with one system in ERA5 that reaches Cat 3. Finally, while the relative agreement between maximum intensities from Figure 4 holds when considering the global scale, results can vary for different basins as seen on Figure B3 from the appendix.

Most notably, the confusion matrix for the NAtl region is the most diagonal and shows very little spread while EPac on the contrary presents very little consistency with most systems in ERA5 being classified as TS, independently of the observed intensity. As also shown by Figure 3, Figure 5 highlights the low wind intensity in ERA5 with a mean maximum wind speed at day 0 of 23.2 m.s -1 against 38.1 m.s -1

Life cycle

in IBTrACS. Furthermore, the relative standard deviation -defined as the ratio of the standard deviation over the sample mean at each time step -is in fact smaller for ERA5 as it ranges from 24 % to 31 % against 34 % to 50 % for IBTrACS, with day 0 having the lowest relative standard deviation for both datasets.

The shapes of both life cycles are distinguished by a few key differences which are better shown in the bottom panels of Figure 5, representing the change rate of the mean wind speed. The life cycle in ERA5 is almost linear outside of the [-2; +2] day range, with the intensification phase having a slightly steeper slope than during the weakening phase and also exhibits a sharp spike around day 0 with great symmetry. In IBTrACS however, the intensification phase is curvilinear with an almost linearly increasing change rate before day -2. The peak around day 0 is not symmetric with the left side showing a sharp intensification until the maximum, followed by a slower decrease and the tipping point occurs slightly later than day 0. This dissymmetry comes solely from the method used to identify the time step of the maximum wind speed; here we select the first occurrence in cases where the maximum wind speed is found on consecutive time steps. Using the last occurrence would yield a mirrored effect, while selecting the central occurrence would even it out. But this subtlety nevertheless illustrates one of the main difference between TCs from the reanalysis and those from the observations, in that TCs in ERA5 appear to be unable to plateau at their maximum intensity like they do in IBTrACS.

Figure 6 represents the distributions of the delays between life cycle from each pair of tracks and for each SSHWS class. A positive lag between two life cycles indicates that the reanalyzed TC reached its maximum intensity later than in IBTrACS. The mean delay across all categories is of 13.3 hours, i.e slightly greater than two time steps (significantly different from 0). Distributions for each class appear to shift towards higher values with TS presenting a 7.7 hours average lag. Starting with Cat 2, the mean lag reaches the overall average and extends to 44.7 hours for Cat 5. All intensity groups have a mean statistically significantly different from 0 under a 5 % confidence level twosided student test. This shift towards higher delays in maximum intensities between ERA5 and IBTrACS could be explained by the lack of rapid intensification in the reanalysis. It should be noted that in cases where the maximum wind speed occurs on several consecutive time steps in IBTrACS -and contrary to the method used for Figure 5 -we select the central occurrence (or the one before if the plateau extends over an even number of time steps) so that we measure the lag with respect to the middle of the maximum intensity plateau. This is done in order to improve the interpretability of these results.

The choice of the occurrence to use in such cases acts in fact as an offset to all distributions but has virtually no impact on the placement of each box plot relative to each other and to the overall mean.

Internal structure

Lastly, figures 7 and 8 show the composite azimuthally averaged cross-section of detected TCs in the reanalysis for both the radial and tangential wind as well as the temperature anomaly and for each SSHS intensity class. These composites are aggregated based only on the intensity of the observed track (i.e

IBTrACS) for matched tracks, independently of how intense the TC is in the reanalysis at those times. The amount of averaged time steps for each SSHS category are presented in Table 2. With this methodology, these composite figures therefore show us the average ERA5 representation of an observed TC from a given SSHS category. The radial wind is seen to move towards the center near the surface, from 1000 hPa up to 900 hPa and then flows outwards radially in the upper part of the atmosphere from 200 hPa up to 100 hPa which demonstrates the low level convergence and the upper-level divergence. Radial wind speed varies from -4.3 to 3.9 m.s -1 for the TS group and increases up to 9 m.s -1 for Cat 5 in both directions. The tangential wind speed profile tightens around the core as intensity increases. The maximum wind speed is located at the 900 hPa level and 150 km from the center for TS, within the 15 -18 m.s -1 range, and is located at 925 hPa and 100 km from Cat 3 to 5, for which it reaches the 27 -30 m.s -1 bin with a maximum of 28.9 m.s -1 . The temperature anomaly from Figure 8 exhibits the same tightening around the core as for the tangential wind. This tightening can be quantified in terms of mean radial temperature gradient taken at the pressure level of maximum anomaly and computed until the anomaly decreases by 50 %, thus representing the temperature anomaly decrease rate close to the core. The mean radial temperature gradient decreases with SSHS categories from -0.69 K per 100 km for TS down to -2.31 K per 100 km for Cat 5. Values for the remaining SSHS categories are presented in Table 2. Maximum temperature anomalies range from 2.74 K for the TS group up to 5.16 K for Cat 5. The maximum temperature anomaly increases for each SSHWS category except for Cat 4 where it drops from 5.14 K (Cat 3) to 4.87 K (Cat 4). A similar decrease in intensity from Cat 3 to Cat 4 is also noted for the tangential wind speed (Table 2). The fact that this decrease can be seen in both variables is expected because the warm core from a TC is linked to its primary circulation through the gradient and thermal wind balance [START_REF] Willoughby | Gradient Balance in Tropical Cyclones[END_REF].

It is however interesting to note that the temperature radial gradient does not follow the same pattern as it either increases or remains constant throughout all SSHS categories. Lastly, the height of the maximum temperature anomaly, defined as the level at which maximum temperature anomaly at 25 km from the center is reached, also increases with intensity. The maximum temperature anomaly is located at 400 hPa for TS, 350 hPa for Cat 1 and 2 and 300 hPa for Cat 3 to 5.

The smooth intensity increase with SSHS categories depicted here is however to be nuanced by the fact that these composites are the result of strong averaging -first azimuthally and then over many time steps and different systems -which tends to smooth out imperfections. Figure B4 from the appendix present the distributions of the maximum tangential wind speed and maximum temperature anomaly for each radius-height cross-section and for each SSHS category and show a large intensity spread in each class, as also pointed out by Figure 4. For instance, the 5 th percentile for the Cat 5 maximum tangentiel wind speed is 19.6 m.s -1 , which is halfway between the mean maximum wind speeds from the TS and Cat 1 categories. Conversely, the TS category exhibits a 95 % percentile of 30.8 m.s -1 , which is stronger than the mean Cat 5 maximum wind speed from Table 2.

Overall, the visual differences in internal structure for all composite variables from Figures 7 and 8 are the strongest from Cat 1 to 3. This includes both the tightening of the wind vortex with SSHWS category, the tightening and elevation of the warm core and also the strengthening of the radial fluxes from the radial wind composites. Above Cat 3, TCs' internal structures produced by ERA5 become almost indistinguishable, as also highlighted in Table 2. This inability to distinguish TCs above Cat 3 is consistent with ERA5 underestimation of TC intensity in general, as seen from Figures 3, 4 and5. Combined with the aforementioned uncertainty in the intensity of reanalyzed TCs, this effect could likely explain the apparent drop in intensity between Cat 3 and Cat 4.

Discussion and Conclusion

As a preliminary part of our work, we conducted a sensitivity analysis of the CNRM TC Tracking Scheme to its five different detection thresholds with respect to the FAR and POD metrics, which is described in Appendix A.

The ultimate purpose of the analysis was to use this information to make an informed choice of threshold values for the complete ERA5 tracking, such that both detection metrics would be optimized to some extent. The analysis provided interesting insights about the tracking scheme with the maximum surface wind and core temperature anomaly thresholds (RES and TANOM)

having the biggest influence on the tracker's performance. Most strikingly, it showed that lowering RES to 10 and 5 m.s -1 consistently improved FAR and POD compared to RES=15. This is irregular since most tracking schemes tend to use wind speed thresholds of at least 15 m.s -1 [START_REF] Walsh | Objectively Determined Resolution-Dependent Threshold Criteria for the Detection of Tropical Cyclones in Climate Models and Reanalyses[END_REF]Ullrich and Zarzycki, 2017, Appendix B). But this is not to say that a 5 m.s -1 wind speed is considered cyclonic in ERA5. In fact, the RES threshold only has a limited impact on the wind speed distribution from the final set of tracks.

Indeed, while 10 m.s -1 can be seen as a more physically acceptable value for the surface wind speed threshold when considering the underestimation of TC intensity in ERA5, it must be noted that the wind speed distributions from the RES=5 and RES=10 non-paired set of tracks in the NAtl basin -all other thresholds held constant -are almost identical (not shown), which is consistent with the observed POD saturation on Figure A1 at RES=10. The mean wind speeds from these two distributions are non-significantly separated by 0.1 m.s -1 (p-value = 0.2). The amount of values strictly below 10 m.s -1 are 5.3 % and 4.9 % for RES=5 and RES=10, respectively. For RES=15, the mean wind speed is only 1 m.s -1 greater that that of RES=10. The reason for the low impact of the RES parameter on the measured wind speed distribution is twofold. First, during the detection process, all detection criteria must be held for at least 4 time steps for the track to be kept, meaning that any track not maintaining a maximum wind speed greater than RES for at least 24 hours will be discarded. The second reason is that the relaxation process completes tracks forward and backward with no other requirement than a set relative vorticity threshold and is intended to capture the genesis and ending of TCs, where wind speeds are the weakest. This particular aspect results in the lower end of the wind speed distributions to remain about the same, independently of the RES parameter. Lowering this threshold therefore mostly affects the number of detected tracks, which is why it acts as a POD upper bound.

The performances of the tracking scheme on ERA5 from 1981 to 2019presented in Section 3.1 -show FAR and POD that vary from basin to basin, with a 67 % mean POD and 24 % mean FAR. Bourdin et al ( 2022) conducted an inter-comparison analysis of four tracking schemes on ERA5, including our own, for which we provided a set of tracks produced with the same parameters than here and uses a post-processed sub-tropical jet (STJ) diagnostic as cut-off latitude to filter out mid-latitude systems, which is applied over all four tracking schemes. This study places the CNRM TC Tracking Scheme's probability of detection on par with the OWZ [START_REF] Tory | The Development and Assessment of a Model-, Grid-, and Basin-Independent Tropical Cyclone Detection Scheme[END_REF] and UZ [START_REF] Ullrich | TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets[END_REF] tracking schemes, both in terms of globally averaged POD and interregional differences. However, without any form of mid-latitude filtering, both these tracking schemes present considerably less false alarms than the CNRM scheme. Moreover, the STJ filter used on our tracking scheme in [START_REF] Bourdin | Intercomparison of four algorithms for detecting tropical cyclones using ERA5[END_REF] offers similar performances to the VTU method in terms of false alarms but has the benefit of preserving the probability of detection, particularly in the NAtl basin. However, The VTU filter presents the interesting property of being dependent only on the instantaneous and local state of the atmosphere whereas the STJ filter requires the use of temporal smoothing over large scale fields. This property of the VTU method makes it possible to implement this criterion as an online diagnostic within a tracking algorithm rather than as a post-processing.

In Sections 3.2 to 3.4 we presented our results on the physical representation of TCs in the ERA5 reanalysis. These results highlighted the underestimation of TC induced wind speeds in ERA5. This underestimation is in fact not specific to ERA5 but concerns all reanalyses, even those (to a lesser extent) with the TC-specific assimilation techniques mentioned in Section 1, and cannot solely be explained by the coarse grid resolution of these products [START_REF] Schenkel | An Examination of Tropical Cyclone Position, Intensity, and Intensity Life Cycle within Atmospheric Reanalysis Datasets[END_REF][START_REF] Hodges | How Well Are Tropical Cyclones Represented in Reanalysis Datasets[END_REF][START_REF] Zarzycki | Metrics for Evaluating Tropical Cyclones in Climate Data[END_REF]. Moreover, we note that the wind-pressure relationship measured in ERA5 (Figure 3) appears somewhat degraded compared to those found with two predecessors of ERA5:

ERA-40 and ERA-Interim [START_REF] Murakami | Tropical cyclones in reanalysis data sets[END_REF][START_REF] Hodges | How Well Are Tropical Cyclones Represented in Reanalysis Datasets[END_REF] due to the discrepancy in how well both the wind speeds and SLPs are represented in ERA5. Therefore -and while the exact origin of the underestimation remains unknown at this point -these elements could hint towards an issue with the model's physics.

We also showed in Section 3.3 that TC intensification rate prior to peak intensity was greater in the best-track catalog than in ERA5, which is consistent with the results from [START_REF] Schenkel | An Examination of Tropical Cyclone Position, Intensity, and Intensity Life Cycle within Atmospheric Reanalysis Datasets[END_REF] obtained on five reanalyses (including ERA-40 and ERA-Interim). However, we find that while all categories of ERA5 TCs reach their peak intensity later than in IBTrACS when looking at the mean lag, the first quartile for the TS and Cat 1 groups below 0 indicate that a considerable amount of TCs peak earlier than in IBTrACS in these categories. This distinction may be the result of differences in methodologies compared to [START_REF] Schenkel | An Examination of Tropical Cyclone Position, Intensity, and Intensity Life Cycle within Atmospheric Reanalysis Datasets[END_REF], mainly with regard to the identification of the time of maximum intensity in IBTrACS, as discussed in Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker Section 3.3, but could also partly result from the increase in horizontal resolution in ERA5 compared to the reanalyses used in [START_REF] Schenkel | An Examination of Tropical Cyclone Position, Intensity, and Intensity Life Cycle within Atmospheric Reanalysis Datasets[END_REF].

Nevertheless, this result is consistent with how ERA5 appears to represent low intensity TCs better than those from higher categories. [START_REF] Zick | Tropical cyclones in the North American Regional Reanalysis: An assessment of spatial biases in location, intensity, and structure[END_REF] analyzed the internal structure of TCs in both the North American Regional Reanalysis (NARR) and CFSR datasets by computing the mean composited radial-height cross section over the ten most intense TCs (according to the reanalyses) and highlighted key structural differences between both. Interestingly, the temperature anomaly cross-section in ERA5 (Figure 8) has similar features to that from NARR -namely, the distinct upper-tropospheric peak and the secondary, weaker, mid-level warming -while the ERA5 tangential wind speed cross-section (Figure 7, right panel) shares more similarity with that from CFSR with a vertical distribution of the maximum wind speed. As stated by [START_REF] Zick | Tropical cyclones in the North American Regional Reanalysis: An assessment of spatial biases in location, intensity, and structure[END_REF], this vertical distribution of the tangential wind speed is consistent with observational studies based on dropwindsonde and Doppler radar data [START_REF] Franklin | AIRS-observed warm core structures of tropical cyclones over the western North Pacific[END_REF][START_REF] Stern | On the Vertical Decay Rate of the Maximum Tangential Winds in Tropical Cyclones[END_REF]. Furthermore, the positive correlation between the altitude of the maximum temperature anomaly and TC intensity (Table 2) has also been documented by observational studies [START_REF] Zhang | Evaluating the Impact of Improvements in the Boundary Layer Parameterization on Hurricane Intensity and Structure Forecasts in HWRF[END_REF][START_REF] Franklin | AIRS-observed warm core structures of tropical cyclones over the western North Pacific[END_REF]. More recently, Wang and Jiang (2019) produced a 13-year climatology of TC warm-core structures based on data from the Atmospheric Infrared Sounder onboard the AQUA satellite, from which they derived typical warm-core heights for each SSHS intensity class. We find that compared to the aforementioned study, the warm core in ERA5 do not rise as high, capping at 300 hPa against 150 hPa for the observations. However, we acknowledge that there is no consensus about the link between warm-core elevation and TC intensity to this day [START_REF] Stern | On the Height of the Warm Core in Tropical Cyclones[END_REF]. Moreover, it is important to remind the reader that the selected compositing times here are based on the observed intensity rather than on the reanalysis. Bearing in mind that the delay between the maximum intensity of ERA5 and IBTrACS TCs increases with

IBTrACS TC intensity, it is possible that replicating this analysis according to the model's intensity would yield slightly different results. For instance, warm core elevation for Cat 4 and 5 could increase, and the decrease in intensity between Cat 3 and Cat 4 would also likely disappear. At any rate, the inability to distinguish TC intensity from their internal structure above Cat 3 in the averaged composites, combined with the large intensity spread associated with reanalyzed TCs within any given SSHS group adds as many extra sources of uncertainty for users wanting to study the internal structure of historical TCs.

Despite some discrepancies in the representation of TCs in ERA5, the reanalysis can still be a valuable tool for the study of tropical cyclones and is capable of producing substantially stronger TCs than ERA-I. It also offers good spatial and temporal homogeneity whereas the differences in detection procedure and maximum wind speed measurement techniques between the meteorological agencies that contribute to IBTrACS produce substantial heterogeneity within the best track catalog [START_REF] Schreck | The Impact of Best Track Discrepancies on Global Tropical Cyclone Climatologies using IBTrACS[END_REF][START_REF] Hodges | How Well Are Tropical Cyclones Represented in Reanalysis Datasets[END_REF]. This is of course to be nuanced by the fact that the quality of observations assimilated in ERA5 also improves with time [START_REF] Of | Can climate trends be calculated from reanalysis data?[END_REF][START_REF] Rienecker | Atmospheric Reanalyses-Recent Progress and Prospects for the Future[END_REF] and that not all regions have the same observational coverage. For instance, the Northern Hemisphere benefits from aircraft reconnaissance as well as from a large network of ground stations in addition to satellite observations, the latter being the primary data source in the Southern Hemisphere.

One could consider using bias correction techniques to alleviate bias in wind speed and minimum SLP values. This is however not trivial as most bias correction methods are rank-preserving (e.g. quantile-quantile) and we showed with Figure 4 that the reanalysis does not preserve the observed rank of TCs.

One other strength of atmospheric reanalyses lies within the fact that they allow not only to study the 3D variables associated within the TC itself (as was showcased in this paper), but also the large scale environment in which TCs occur. In both cases, being able to locate precisely TCs in the reanalysis is essential as TC position between a reanalysis and IBTrACS rarely coincide perfectly, hence the need for an objective TC tracking scheme. ERA5 continues on the path of improvement, following in the footsteps of its predecessors, in part due to its increased horizontal resolution, but TC representation would undoubtedly benefit from the use of TC-specific treatments.

Appendix A Tuning the tracker's detection thresholds

In this section we present a sensitivity analysis of the tracking scheme to its detection thresholds in order to identify a set of tracking parameters (see 2.2) that maximizes the efficiency of detection while limiting the amount of false alarms. In such cases where two or more objectives are to be simultaneously optimized, no single solution can be derived as there is a trade-off between the objectives. One can however aim to approach a solution that is said to be Pareto efficient if one of the objectives cannot be improved further without degrading the other [START_REF] Zitzler | Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach[END_REF][START_REF] Deb | The ERA-Interim reanalysis: configuration and performance of the data assimilation system[END_REF].

To find such a solution, we explore the parameters' space by perturbing the tracking parameters so that they can each take three different values, thus forming a set of 243 combinations of five thresholds, called vectors or solutions.

The values of each parameters are presented in Table A1. For each vector, our tracking scheme is applied on both the North Atlantic (NAtl) region -which is the best observed basin -and the South Indian (SInd) basin -which is under Météo-France forecasting responsibility -from season 2008 to 2018. We justify why the conclusions drawn from two basins can be largely be applied to other regions at the end of Section A. We then proceed to pair the detected tracks with IBTrACS, following the methodology from 2.3 and compute the FAR and POD integrated over the 11-year period.

Figure A1 presents each combination of thresholds in FAR / POD space for both basins, called the objective space. In the objective space, an ideal solution maximizing the POD while minimizing the FAR would be located in the upper left corner of the plots, i.e. POD=100 % and FAR=0 %. However such a solution does not exist here as FAR and POD appear to be related to each other through a non-linear relationship such that improving the POD Table A1 Detection thresholds values tested for the sensitivity analysis. Combinations of these values form a set of 3 5 vectors. The boxed value in each row denotes the value that was selected for the corresponding parameter to conduct the complete ERA5 tracking. These plots also inform us on how each threshold affects the performance of the tracking scheme in terms of these two metrics. For the VOR, most vectors are stacked on top of each other, indicating that the vorticity threshold has little impact on the tracker's performance, or that the sampling on this criterion was not selective enough. However, and while the effect cannot be seen visually here, higher VOR value appears to be associated with a lower FAR for vectors with high PODs. Indeed, vectors with a POD greater than 70 % show a 1 % relative decrease in FAR with VOR set at 15•10 -5 s -1 compared to 5•10 -5 s -1 in the NAtl basin, and a 1.3 % relative decrease for SInd. This tends to show that the vorticity threshold may act as a false alarm filter. However, increasing the VOR too much would inevitably lead to a loss in POD as weaker TCs would fail to meet the criterion. Therefore we considered that the gain from testing with higher values would have been marginal with respect The temperature anomaly threshold (TANOM) stratifies the FAR as each value taken by this parameter corresponds to a certain FAR range with little overlapping. Reducing the threshold leads to an increased FAR, as it allows the tracker to detect cooler systems. However, because of the link between FAR and POD, an increased FAR generally implies a higher POD, making TANOM=1 vectors prime choices for finding a good performing solution within our set.

Criterion

As for the last two detection thresholds that define the strength of vertical profiles for respectively the temperature and horizontal wind speed (PT and PW), they act by design as filters for extra-tropical cyclones -counting as false alarms in our methodology -by ensuring the presence of respectively a warmer upper core and stronger near-surface winds. Both of these properties are indeed reversed in extra-tropical cyclones, and linked together by the thermal wind relationship. No distinct pattern applicable to both regions and all vectors emerge from the analysis. However, when considering only the group of vectors with the highest POD in each region, increasing these thresholds tends to deteriorate the FAR with marginal benefit to the POD.

As a result, we chose VOR=15 for its potential to reduce the amount of false alarms, RES=5 for the extended POD upper bound, TANOM=1 because of the advantageous location of these vectors in the objective space for the NAtl basin and selected a solution from the remaining candidates in our set which we felt constituted a satisfying compromise in both regions -leaving us with PT=1 and PW=5. The choice of a 5 m.s -1 wind speed threshold and its meaning for the CNRM TC Tracking Scheme is further discussed in the Disccusion section (Section 4). This particular solution exhibits a 54 % FAR and 72.4 % POD in the NAtl region, and a 41 % FAR and 81.2 % POD in the SInd basin. The important amount of false alarms in the chosen solution, the majority of which being mid-latitude systems, motivated us to then add the VTU post-processing method described in Section 2.2 as a mean of improving performances even further. Applying the VTU method on the selected solution brings down the FAR to respectively 15 % and 27 % for NAtl and SInd between 2008 and 2018. This reduction in FAR comes at a cost to the POD in the NAtl basin which is lowered to 58 %. However, the POD in the SInd remains unaffected by the use of the additional filter, which is likely due to the fact that there is little TC activity at mid-latitudes in this region.

Finally, it is worth noting that while our test vectors in the SInd region present generally higher PODs in this experiment (14 points more in average) and are arranged differently in the objective space than in the NAtl region, the rankings of each vector on FAR and POD scales is approximately preserved between both region, as can be seen in Figure A2 below.

POD ranks between both basins indeed show a 0.96 correlation coefficient FAR ranks are at 0.98, meaning that any given vector performs about as well in both basins with respect to the other vectors. This gives us confidence in the fact that tuning our algorithm over two basins only can be relevant for all basins.

Appendix B Supplementary figures

  FigureB1from the appendix presents the basins boundaries, which are adapted from[START_REF] Knutson | Tropical Cyclones and Climate Change Assessment: Part II: Projected Response to Anthropogenic Warming[END_REF] so that the separation between the Easter Pacific basin (EPac) and the North Atlantic basin (NAtl) follows the American coast. For each detected track in a given basin, we start by searching for potential matches in IBTrACS by selecting tracks that have at least one time stamp in common (temporal overlapping). Then for each candidate track, we compute the great-circle distances between the detected track's positions and the candidate's at equal time stamps. A detected position is considered to be found in IBTrACS if the great-circle distance between ERA5

Fig. 1

 1 Fig. 1 FAR and POD over the 1981 -2019 seasons for all five basins of interest. Dotted lines represent the averages over these regions.

Fig. 2

 2 Fig. 2 Probability density map of passage of an ERA5 track for (left) one that is matched to IBTrACS and (right) one that is flagged as false alarm. Computed through kernel density estimation with an anisotropic Gaussian kernel with respectively 3°and 1°zonal and meridional standard deviation over a 0.25°resolution spatial grid.

  Figure3represents the wind-pressure relationship (WPR) for the set composed of all temporally and spatially matching points in each track pair, as described in 2.3. Using a V = a∆P b relationship[START_REF] Atkinson | Tropical Cyclone Minimum Sea Level Pressure/Maximum Sustained Wind Relationship for the Western North Pacific[END_REF] to fit on both groups of points, we get a = 3.15 and b = 0.54 for ERA5 and a = 1.4 and b = 0.8 for IBTrACS. Both relationships are therefore vastly different with a faster falling SLP curve in ERA5 compared to IBTrACS. As a result, the wind speeds in ERA5 are considerably lower than in IBTrACS with a 95 th percentile of 28.5 m.s -1 for the reanalysis -which corresponds to a tropical storm on the Saffir-Simpson Hurricane Wind Scale (SSHWS) -compared to 50.5 m.s -1 for the observational records.

Fig. 3

 3 Fig.3Pressure-Wind relationship from the subset of matching points in all paired tracks. 58,920 data points in each group. Solid lines show the V = a∆P b fits and the reference SLP at the intercept is set to be greater than the highest SLP record. The marginals represent the density distributions of the data projected on each axis. Horizontal dotted lines represent the pressure-based Saffir-Simpson Hurricane Scale (SSHS) intensity class thresholds from[START_REF] Klotzbach | Surface Pressure a More Skillful Predictor of Normalized Hurricane Damage than Maximum Sustained Wind[END_REF]

Figure 4

 4 Figure 4 then illustrates the relationship between ERA5 and IBTrACS SSHPS intensity classes by classifying TCs from paired tracks over their entire life cycle. A general agreement between the intensity classes of both datasets is apparent with the majority of entries located below the main diagonal, which is

Fig. 4

 4 Fig. 4 Confusion matrix between ERA5 and IBTrACS SSHS intensity classes over the five basins of interest. TCs are categorized using the pressure-based SSHS intensity thresholds from Klotzbach et al (2020).

Figure 5

 5 Figure 5 compares the average lifecycle between matched TCs from ERA5 and IBTrACS, centered around the maximum wind speed at day 0. The sample sizes (top panels) are of different shape around the center due to ERA5 tracks not necessarily having the same length as their IBTrACS match, thus indicating that detected tracks in the reanalysis tend to be shorter than in IBTrACS.

Fig. 5

 5 Fig. 5 Mean TC life cycles in (left) ERA5 and right (IBTrACS) from the set of matching tracks. Life cycles are centered around the time of maximum wind speed and integrated over the five basins of interest. Both upper panels show for each time step the size of the sample used to compute the mean and standard deviation. Lower panels present the derivative of the mean wind speed. The X axis goes from -6.25 to +6.25 days with 0 being the maximum intensity. The Y axes on the bottom row plots are not aligned.

Fig. 6

 6 Fig. 6 Distributions of the maximum intensity delays between paired tracks for each SSHS category. Categories are based on IBTrACS maximum wind speed. The delay is relative to IBTrACS such that a positive lag indicates a later intensity peak in ERA5.

Fig. 7

 7 Fig. 7 Radius-height composites cross-sections of azimuthally averaged radial (left) and tangential (right) wind speed of detected TCs in ERA5 sorted by IBTrACS SSHWS categories. Each panel is made from the set of matching time steps where the observed track is at the given SSHWS intensity. The azimuthal mean is computed within a 41×41 point horizontal box and 27 vertical levels ranging from 1000 to 100 hPa.

Fig. 8

 8 Fig.8As in Figure7but for the temperature anomaly. The reference temperature is computed from the four corners of the 41×41 box, i.e from points 500 to 700 km away from the center.

Fig. A1

 A1 Fig. A1 Objective space representation of each combination of thresholds. The first column shows the results for the NAtl basin and the second column those for the SInd basin. Each of the five rows correspond to a detection criterion. Each point (combination of thresholds, a.k.a vector) is colored according to the value taken by said criterion. In each panel, the red-bordered star denotes the selected vector.

  degrade the FAR, and vice versa. In fact, points located along the leftmost of the scatter plots indicate the best trade-off between POD and FAR based on our sample. Choosing a solution among the ones presented here is therefore a subjective choice expressing a balance between detection efficiency and false alarms rate.

  to the cost of conducting new experiments. Moreover, the final set of tracks from the global tracking made with the threshold values from TableA1show a 0.1 % percentile of the maximum vorticity per matched track of 22•10 -5 s -1 which tends to support this claim.The surface wind speed threshold (RES) on the other hand has a clear impact on performances as it effectively defines an upper bound for the POD. Setting RES to 15 m.s -1 bounds the POD to 54 % in NAtl and 67.3 % in SInd. Boundaries formed by vectors with RES values set to 10 and 5 m.s -1 are located further up the POD axis and are capped respectively to 74.8 % and 76.7 % for NAtl and 83 % and 83.6 % for SInd. The sensitivity to the RES parameter shows in fact that the POD saturates at RES=10, with RES=5 vectors offering only marginally better PODs in the NAtl basin, and no apparent change in the SInd basin. Because of the POD gap between RES=10 and RES=15, it is possible that the saturation value is actually located between 10 and 15 m.s -1 .

Fig. A2

 A2 Fig.A2The left column places each vector POD-wise (upper left) and their respective ranks -from 1 to 243 -between both experiments (bottom left). The right column does the same FAR-wise. PODs are ranked in ascending order such that higher POD values have a higher rank and FARs in descending order. The red line on the bottom row panels show the fitted line between NAtl and SInd ranks and exhibit a 0.97 and 0.94 correlation coefficient for POD and FAR, respectively.

Fig. B1

 B1 Fig. B1 Geographical basins definition. Adapted from Knutson et al (2020)

Fig. B3

 B3 Fig. B3 Per basin contingency crosstables between ERA5 and IBTrACS pressure-based intensity classes.

Fig. B4

 B4 Fig. B4 Distributions of (a) the maximum tangential wind speed per composited radiusheight cross-section for each observed SSHWS intensity class, and for the maximum temperature anomaly (b).

Table 1

 1 Statistical summary of yearly FAR and POD timeseries from 1981 to 2019

		FAR (%)		POD (%)
		5 % 95 % mean	std 5 % 95 % mean	std
	Basin
	EPac	0.0	38.6	20.4 11.3 30.1	89.1	62.2 17.3
	NAtl	0.0	43.6	21.4 16.2 28.2	79.1	54.7 16.1
	SInd	0.0	44.7	18.7 12.8 46.3 100.0	74.5 18.6
	SPac	17.5	66.6	43.9 18.7 34.9 100.0	67.8 21.2
	WPac	3.1	27.4	14.1	7.6 64.3	93.1	78.5	9.9
	Global 15.4	29.1	21.8	4.6 53.5	79.2	69.2	8.5

Table 2

 2 The

		TS	Cat 1 Cat 2 Cat 3 Cat 4 Cat 5
	Count	27,047 10,065 6,514 3,953 1,894	205
				Tangential Wind Speed
	Maximum wind speed (m/s)	16.5	22.6	27	28.7	28.6	29.7
	Height of max. wind speed (hPa)	900	900	900	925	925	925
	Radius of max. wind speed (km)	150	125	125	100	100	100
				Core Temperature Anomaly
	Maximum anomaly (K)	2.74	3.99	4.89	5.14	4.87	5.16
	Height of max. anomaly (hPa)	400	350	350	300	300	300
	Radial gradient (K per 100 km)	-0.69 -1.22 -1.73 -1.89 -1.88 -2.31

amount of time steps that are averaged to produce the radius-height composites from Figures

7 and 8

, for each SSHS category. Below are presented general characteristics for the tangential wind speed and core temperature anomaly for all intensity categories.

Declarations Funding

This work was partly supported by the French National program LEFE (Les Enveloppes Fluides et l'Environnement), specifically by the CYPRESSA project.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Author contributions

WD carried out the study under the guidance of JC and FC. FC provided the tracking scheme. SB provided the VTU post-processing. WD did the figures and wrote the manuscript. All authors provided critical feedback and contributed to the revisions.

Data availability

The ERA5 data used was obtained from the ESPRI Mesocentre from the IPSL, Polytechnique, but is otherwise available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The IBTrACS catalog is available at https://www.ncei.noaa.gov/products/international-best-trackarchive. The tracking and track pairing data generated and analysed during the current study are available from the corresponding author on reasonable request.


Anisia Popescu
email: anisia.popescu@universite-paris-saclay.fr

Mathilde Hutin

Ioana Vasilescu

Lori Lamel

Martine Adda-Decker

STOP DEVOICING AND PLACE OF ARTICULATION: A CROSS-LINGUISTIC STUDY USING LARGE-SCALE CORPORA

Keywords: large corpora, voicing maintenance, automatic alignment, pronunciation variants, Romance languages

whether they are published or not. The documents may come

INTRODUCTION

Maintaining voicing requires sufficient transglottal airflow. In stops, air accumulates in the oral cavity, reducing the oral and subglottal pressure gradient which may lead to insufficient transglottal airflow and result in voicing loss. This is known as the Aerodynamic Voicing Constraint (AVC) [START_REF] Ohala | The origin of sound patterns in vocal tract constraints[END_REF][START_REF]Accommodation to the aerodynamic voicing constraint and its phonological relevance[END_REF]. Certain places of articulation are more compatible with voicing than others: It has been shown that voiced stops are more marked the more posterior the constriction location is [START_REF] Gamkrelidze | On the correlation of stops and fricatives in a phonological system[END_REF] (i.e., voiced labials and coronals are more frequent than voiced velars, making /g/ a frequent phonological gap [START_REF] Nikolaev | A typology of consonant inventory gaps[END_REF][START_REF] Sherman | Stop and fircative systems: A discussion of paradigmatic gaps and the question of language sampling[END_REF]). Initially this pattern was attributed to oral cavity volume [START_REF] Martinet | Economie des changements phonétiques[END_REF]: Pressure builds up faster in a posterior constriction location leading to faster voicing loss in velar stops.

Several studies [START_REF] Ohala | Passive vocal tract enlargement during voiced stops[END_REF][START_REF] Javkin | Towards a phonetic explanation for universal preferences in implosives and ejectives[END_REF] have since shown that the difference in cavity volume alone has a negligible effect on pressure differences, and that the pattern is more likely related to the compliant surface available for passive enlargement of the vocal tract: Stops with more anterior places of articulation have more soft tissue available for increasing the vocal tract volume (i.e., lowering the larynx, elevating the soft palate, advancing the tongue root, depressing the tongue body [START_REF] Westburry | Enlargement of the supraglottal cavity and its relation to stop consonant voicing[END_REF][START_REF] Rhotenberg | The breadth-stream dynamics of simple-released-plosive production[END_REF]).

However, most studies investigating voicing alternations as a function of place of articulation are done in controlled, small-scale laboratory studies (e.g., [START_REF] Pape | Stop and Fricative Devoicing in European Portuguese, Italian and German[END_REF][START_REF] Keating | Physiological effects on stop consonant voicing[END_REF]). Large scale cross-linguistic studies are scarce. A recent corpus study investigating the voicing of Danish stops in intervocalic position [13] did not find the predicted pattern supporting an aerodynamic account: The highest rate of voicing was found for coronals, not velars. The present study aims to test whether velar stops are more prone to devoicing than their labial and coronal counterparts in the European variants of five Romance languages (French, Italian, Portuguese, Romanian and Spanish), using large corpora and automatic alignment with pronunciation variants.

METHODOLOGY

Corpora and devoiced stop detection

The corpus consists of more than 1000 hours of French, Italian, Portuguese, Romanian and Spanish journalistic speech from TV and radio shows.These corpora were acquired from the LDC and ELRA, or developed during international projects: IST ALERT for part of the data in French and Portuguese [START_REF] Da Silva | Lattice-based unsupervised acoustic model training[END_REF][START_REF]Interpolation of acoustic models for speech recognition[END_REF]; IRST for part of the data in Italian [START_REF] Després | The vocapia research asr systems for evalita 2011[END_REF][START_REF] Marcello | Development and Evaluation of an Italian Broadcast News Corpus[END_REF] and OSEO Quaero [START_REF] Lamel | Speech Recognition for Machine Translation in Quaero[END_REF][START_REF] Lamel | Multilingual speech processing activities in Quaero: application to multimedia search in unstructured data[END_REF] for all languages. Only European shows were kept in the final corpus. Manual transcription was available for all data, except for Romanian, which only had 7h of manually transcribed data; The reminder of the Romanian data was automatically transcribed using a speech-to-text system [START_REF] Vasilescu | Exploring pronunciation variants for Romanian speech-to-text transcription[END_REF].

An automatic speech recognition (ASR) system [START_REF] Lamel | On designing pronunciation lexicons for large vocabulary continuous speech recognition[END_REF] was used to carry out the forced alignment, matching speech segments to their orthographic transcription using language specific acoustic models and pronunciation dictionaries. A total of 486,366 bilabial, 1,557,644 coronal and 283,328 velar stops were labeled by the system across the five languages. Following [START_REF] Lamel | On designing pronunciation lexicons for large vocabulary continuous speech recognition[END_REF][START_REF] Adda-Decker | Pronunciation variants across system configuration, language and speaking style[END_REF], the dictionaries were enriched with equiprobable pronunciation variants for stop voicing (e.g., the Fr. beaucoup /boku/ 'a lot' had four possible pronunciations: [boku], [poku],

[bogu], [pogu]) allowing the system to choose the variant which best matches the language-specific acoustic models. This method has proven reliable and useful to the investigation of voicing and devoicing in several recent works (Romanian [START_REF] Hutin | Ongoing Phonologization of Word-Final Voicing Alternations in Two Romance Languages: Romanian and French[END_REF], French [START_REF] Jatteau | Gra[f]e!' Word-Final Devoicing of Obstruents in Standard French: An Acoustic Study Based on Large Corpora[END_REF], Spanish [START_REF] Ryant | Large-scale analysis of spanish /s/-lenition using audiobooks[END_REF][START_REF] Vasilescu | Exploring Temporal Reduction in Dialectal Spanish: A Large-scale Study of Lenition of Voiced Stops and Coda-s[END_REF][START_REF] Hualde | Lenition of intervocalic alveolar fricatives in catalan and spanish[END_REF], Italian [START_REF] Hualde | Lenition and phonemic overlap in Rome Italian[END_REF] or several Romance languages [START_REF] Vasilescu | Alternances de voisement et processus de lénition et de fortition: une étude automatisée de grands corpus en cinq langues romanes[END_REF][START_REF] Hutin | Synchronic Fortition in Five Romance Languages? A Large Corpus-Based Study of Word-Initial Devoicing[END_REF][START_REF] Hutin | When phonetics meets morphology: Intervocalic voicing within and across words in romance languages[END_REF][START_REF] Wu | Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages[END_REF]). The rates of devoiced variants detected by the system are given in Tab. 2 and illustrated in Fig. 1

Statistical analysis

Various phonetic and phonological factors have been shown to correlate with loss of voicing. The factors we are considering in this study are place of articulation (PoA: bilabial, coronal, velar), stop duration, language (French, Italian, Portuguese, Romanian, Spanish), position in the word (initial, medial, final), position in the syllable (onset, coda), phonotactic complexity (singleton, cluster) and voicing status of the previous and following segment (pause, voiceless, voiced). The data was Based on previous literature, we make the following predictions for each of our model variables:

• PLACE OF ARTICULATION: higher rates of devoicing in velar than in labial and coronal consonants (velar > coronal > bilabial) [START_REF] Ohala | Passive vocal tract enlargement during voiced stops[END_REF] • STOP DURATION: higher rates of devoicing in longer stops [START_REF] Wu | Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages[END_REF] • LANGUAGE: no specific predictions • POSITION IN WORD: higher rates of devoicing in final position followed by initial and medial positions (final > initial > medial) [START_REF] Vasilescu | Alternances de voisement et processus de lénition et de fortition: une étude automatisée de grands corpus en cinq langues romanes[END_REF] • SYLLABLE POSITION:

higher rates of devoicing in coda position (coda > onset) [START_REF] Ségéral | Positional factors in lenition and fortition[END_REF] • PHONOTACTIC COMPLEXITY: higher rates of devoicing in clusters than in singletons (clusters > singletons) • VOICING ENVIRONMENT: higher rates of devoicing in pause or voiceless contexts (pause > voiceless > voiced) [START_REF] Wu | Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages[END_REF] 3. RESULTS

Results will be presented in two stages. First we report on overall tendencies for all variables (i.e., all five languages pooled together). We then present language-specific devoicing differences related to place of articulation. The reported patterns are given in Tab. 3 and illustrated in Fig. 2, which show the rates of devoiced stops detected by the system for each of our categorical variables (word position, syllable position, phonotactic complexity, voicing status in the previous and following segments) for all five languages.

Variable

Language-specific patterns

When modeling the velar and labial vs coronal contrasts per language (comparing each language with the overall mean of the LANGUAGE variable), we find that the velar contrast (velars compared to bilabials and coronals) is not significant for Italian, Spanish and French (p>0.1). Romanian and Portuguese pattern with the overall direction (velars tend to devoice less than bilabials and coronals), the effect being highest for Portuguese (Por: Est.: -0.106281, t ∼ -4.842, p<0.0001; Rom: Est.: -0.037202, t ∼ -2.204, p<0.05). When looking at contrasts between bilabials and coronals, we find that there is no significant difference in devoicing rates for Romanian and French (p>0.1). Portuguese has significantly higher rates of coronal than bilabial devoicing (Est.: 0.540493, t ∼ 25.003, p<0.0001). Spanish and Italian both show patterns that support aerodynamic accounts of voicing maintenance: Bilabials tend to devoice more than coronals in both languages, when compared to the overall mean (Ita: Est.: -0.417066, t ∼ -20.304, p<0.0001; Spa: Est.: -0.025293 t ∼ -2.090, p<0.05).

To sum up, velars do not devoice at higher rates than bilabial and coronal consonants: No significant effect was found for Italian, Spanish and French and the opposite trends (i.e., velars devoice less than bilabials and coronals) was found for Portuguese and Romanian. The labial vs coronal contrast seems to be more language-specific: In Italian and Spanish, bilabials devoice at higher rates than coronals, Portuguese shows the opposite trend (coronals > and Romanian and French show no difference between bialabial and coronal devoicing rates when compared to the overall mean.

4.

The present study investigates whether there are any differences in stop voicing maintenance based on place of articulation in the European varieties of five Romance languages. More than 1000 hours of journalistic speech were automatically aligned using pronunciation variants, which allow the system to automatically detect devoiced stops based on language-specific automatic speech recognition (ASR) systems. By analyzing the effect of place of articulation along with other phonetic and phonological factors, we find that our predictions based on aerodynamic accounts of voicing maintenance are not confirmed: Stops with more posterior constriction locations (velars) are not more prone to voicing loss. Instead, in line with previous studies [13], we find that coronals tend to devoice at higher rates (a trend that is mainly driven by the high coronal devoicing in Portuguese). Several explanations can be put forward. First, coronals tend to be more frequent than bilabials and velars, and articulatory reduction phenomena (e.g., glottis spreading) tends to occur in more frequent environments. This is also the case for our data (there are twice as many coronal stops than velars and bilabials). Second, results might be influenced by the high rate of coronal devoicing found for Portuguese.

Portuguese has been shown to behave differently than other Romance languages when it comes to voicing patterns [START_REF] Pape | Stop and Fricative Devoicing in European Portuguese, Italian and German[END_REF][START_REF] Hutin | When phonetics meets morphology: Intervocalic voicing within and across words in romance languages[END_REF]. Another explanation could be that in languages which require actual vocal fold vibration to cue the voicing-contrast, such as Romance languages (with the possible exception of Portuguese), speakers might adapt their articulatory strategies to circumvent the AVC and maintain the voicing contrast, as suggested by [START_REF] Solé | Articulatory adjustments in initial voiced stops in spanish, french and english[END_REF]. In other words, the articulatory goal of vocal fold vibration for voiced stops [START_REF] Browman | Articulatory gestures as phonological units[END_REF] is achieved by compensating for the aerodynamic constraints.

Finally, there is also the issue of methodology. The present study relies on language dictionaries with pronunciation variants to detect devoiced stops. When selecting the appropriate variant the ASR system relies on acoustic features, which aren't accessible to us.

Our analysis of the variant selection is mostly phonological.

To thoroughly investigate the relationship between place of articulation and voicing maintenance, as well as language-specific differences within a single language family, large-scale acoustic studies of the data are planned. Acoustic analyses (e.g., voicing probability throughout the stop) would give us a clearer picture of the stop devoicing phenomenon. Furthermore, the present analysis should be extended to languages that, similarly to Romance languages, need vocal fold vibration during the stop's closure (Polish, Bulgarian, Catalan, Dutch) as well as to languages that do not rely on voicing during the stop's closure (German, English, Mandarin).

Figure 1 :

 1 Figure 1: Percentages of devoiced stops detected in the corpora as a function of Language (from left to right: French, Italian, Portuguese, Romanian, Spanish) and Place of Articulation (from left to right: bilabial, coronal, velar).

Figure 2 :

 2 Figure 2: Rates of devoiced stops detected in the corpora for each categorical variable in the model.

Table 1

 1 shows the counts of phonemically voiced stops in the corpora for each of the five languages.

		b	d	g	Total
	Fre	25,274 197,543 24,731	247,548
	Ita	34,289 183,571 26,877	244,737
	Por	26,689 119,566 25,125	171,380
	Rom 100,518 398,219 87,399	586,136
	Spa 299,596 658,745 119,196 1,077,537

Table 1 :

 1 Counts of phonemically voiced stops per language: French (Fre), Italian (Ita), Portuguese (Por), Romanian (Rom), Spanish (Spa).

  .

	Language bilabial coronal	velar
	French	6.69%	6.7%	7.01%
	Italian	7.58%	6.11%	8.25%
	Portuguese 8.23% 15.12% 10.03%
	Romanian 6.81%	8.28%	8.43%
	Spanish	7.19%	8.79%	8%

Table 2 :

 2 Percentage of bilabial, coronal and velar devoiced stops per language.

Table 3 :

 3 Rates

of devoiced stops detected in the corpora for each categorical variable in the model.

Speech Corpora and Big Data ID: 429

Speech Corpora and Big Data


Anne D ' Orazio

Véronique Biau

Habitat participatif

L'habitat participatif est une démarche citoyenne qui permet à un groupe d'habitants de s'associer en vue de produire du logement et des espaces mutualisés. Fondée sur un principe de participation aux décisions concernant le montage, la programmation, la conception, la construction puis la gestion d'un ensemble résidentiel, cette démarche fait de la question du partage une dimension forte du projet.

En France, une dénomination sous le signe de la participation

L'habitat participatif est souvent présenté comme une « troisième voie » qui se distingue à la fois du logement social et de la promotion privée traditionnelle. Il emprunte ses références à des modèles développés dans diverses régions du monde sous des termes variés : « habitat groupé » en Belgique, « coopératives d'habitation » en Suisse, « cohousing » dans les pays scandinaves, etc. [START_REF] Bresson | L'habitat participatif en Europe[END_REF].

En France, la filiation des pratiques contemporaines renvoie principalement à l'habitat groupé autogéré qui s'est déployé au début des années 1970 [START_REF] Bacqué | Changer la vie : les classes moyennes et l'héritage de Mai 68[END_REF]. Pour autant, des initiatives antérieures comme les coopératives d'Habitations à Bon Marché, la location-attribution ou les coopératives d'auto-construction du mouvement Castors, avaient émergé dès la première moitié du XX e siècle. L'expression « habitat participatif » résulte des discussions qui ont pris place en vue des premières Rencontres Nationales de l'Habitat Participatif, organisées en 2010 à Strasbourg. C'est cette expression qui a été retenue dans l'écriture de la loi ALUR qui donne à cette pratique, en 2014, son cadre juridique : « L'Habitat Participatif est une démarche citoyenne qui permet à des personnes physiques de s'associer, le cas échéant avec des personnes morales, afin de participer à la définition et à la conception de leurs logements et des espaces destinés à un usage commun, de construire ou d'acquérir un ou plusieurs immeubles destinés à leur habitation et, le cas échéant, d'assurer la gestion ultérieure des immeubles construits ou acquis. En partenariat avec les différents acteurs agissant en faveur de l'amélioration et de la réhabilitation du parc de logements existant public ou privé et dans le respect des politiques menées aux niveaux national et local, l'Habitat Participatif favorise la construction et la mise à disposition de logements, ainsi que la mise en valeur d'espaces collectifs dans une logique de partage et de solidarité entre habitants. » (Art. 47 de la loi pour l'Accès au Logement et un Urbanisme Rénové, du 24 mars 2014, Art. L. 200-1 du Code de la construction et de l'habitation). Ce nouveau cadre juridique institue deux formes de sociétés d'habitat participatif : les coopératives d'habitants et les sociétés d'attribution et d'autopromotion.

Institutionnalisation de la notion et diversification des pratiques

Les premières mobilisations militantes contemporaines voient le jour dès le début des années 2000, portées par une série d'associations locales qui se créent ou se structurent dans différentes régions de France. Elles font largement écho aux initiatives qui sont apparues dans le nord de l'Europe dans le cadre de la dynamique des écoquartiers, ou au Québec autour du logement communautaire coopératif. Très vite, deux tendances principales se font concurrence qui ont toutefois en commun une volonté de construire des alternatives à la promotion immobilière conventionnelle (statut de propriété, production d'espaces communs partagés, implication des habitants dans la gestion patrimoniale, …). D'un côté, les militants de l'approche coopérative (héritiers des expériences québécoises et suisses) revendiquent un nouveau statut de droit et la reconnaissance du principe de la propriété collective. De l'autre, les tenants de l'auto-promotion (inspirés des démarches allemandes des Baugruppen mais aussi des initiatives françaises autogestionnaires des années 1970) souhaitent que le groupe d'habitants s'organise pour endosser le rôle de maître d'ouvrage. Il se livre à lui-même un immeuble d'habitation qu'il aura programmé, financé et qu'il gérera en contrôlant son cadre de vie [START_REF] Bacqué | Habitats alternatifs : des projets négociés ? Rapport de recherche pour le Plan Urbanisme Construction et Architecture[END_REF]. Si les premiers temps furent ceux de la rivalité entre les démarches, très vite la nécessité d'une position commune s'est faite jour, visant l'interpellation des pouvoirs publics (D'Orazio, 2012). L'usage commun du terme d'habitat participatif à partir de 2010 a scellé cette alliance tout en maintenant les spécificités de chacune des approches [START_REF] Carriou | L'habitat participatif, quand les institutions militent[END_REF]. La distinction entre les démarches coopératives d'une part et les démarches d'autopromotion d'autre part a été entérinée à l'occasion des travaux préparatoires de la loi ALUR qui se sont tenus en 2013. A cette occasion, des ateliers ont réuni autour de Cécile Duflot, Ministre du Logement et de l'Egalité des territoires, et de son administration, la Coordin'action (groupement des principales associations militantes), le Réseau national des collectivités locales pour l'habitat participatif (RNCHP), l'Union sociale pour l'habitat (USH), la Fédération des coopératives HLM, et le Réseau des professionnels de l'habitat participatif (RAHP), etc. Au-delà de la reconnaissance en droit de l'habitat participatif, cet exercice singulier de co-construction de la loi a facilité la diffusion et l'institutionnalisation de la démarche (D'Orazio, 2017). Ainsi, en 2015 lors du Congrès HLM, l'USH affirme son engagement et annonce la production à venir d'environ 1500 logements, soit une centaine d'opérations. S'ouvre alors une nouvelle séquence d'hybridation des modèles où les opérateurs sociaux de l'habitat jouent un rôle essentiel dans la légitimation et la fiabilisation des opérations tant auprès des collectivités locales que d'un public plus large et moins initié.

Débats et controverses

L'habitat participatif est de faible ampleur puisqu'en 2022 l'on estime à 900 les groupes-projets dont la réalisation est achevée ou en cours (source : Habitat Participatif France) ; et pourtant, ce phénomène a engendré de multiples débats et voies d'expérimentation.

• L'habitat participatif apparaît comme une alternative aux voies traditionnelles de production du logement, mettant l'habitant-usager en situation de « consom'acteur » (Dubuisson-Quellier, 2009). En cela, il répond à un air du temps plus soucieux de la prise en compte des usages dans la conception architecturale et urbaine et rouvre l'éventail des possibles dans une production du cadre habité qui s'est progressivement enfermée dans un cadre réglementaire, normatif et financier très restreint conduisant à une standardisation des formes de l'habitat [START_REF] Costes | Habiter autrement ?[END_REF]. L'habitat participatif est ainsi l'occasion pour les architectes d'inventer de nouvelles interactions avec les futurs habitants, tant en phase de définition des attentes et donc de programmation, que dans la conception des espaces partagés ou enfin dans le dessin des espaces privatifs. On peut toutefois se demander si, engagé à son origine dans une perspective très militante, le phénomène « habitat participatif » ne s'est pas fondu au cours des années dans des pratiques plus ordinaires. C'est le cas par exemple de la place qu'ont prise progressivement des acteurs professionnels venant en appui aux groupes de ménages ou en se substituant à eux dans leur rôle de maîtres d'ouvrage. En revanche, cette ouverture du jeu d'acteurs favorise la diffusion de ces démarches auprès de ménages socialement plus diversifiés.

Par ailleurs, il sera intéressant d'observer si à l'avenir, dans les projets développés par les promoteurs privés notamment, le rôle du groupe des habitants ne tend pas à se réduire, les moments de co-programmation et de co-conception perdant de leur importance dans les processus de production. L'enjeu lié aux espaces partagés semble s'atténuer au profit d'une valorisation du « logement choisi », « sur-mesure », s'adressant à des ménages consommateurs d'alternative et de distinction.

• Dans ses diverses formes, l'habitat participatif questionne les conditions et les parcours d'accès au logement, le juste prix de celui-ci dans le budget des ménages, et cherche à s'affranchir des phénomènes de spéculation immobilière. Les coopératives d'habitants ouvrent également un débat critique sur la propriété individuelle de la résidence principale, sur les modalités de la transmission du patrimoine immobilier par succession, ainsi que sur la plus-value foncière et immobilière engendrée par sa revente. Elles instaurent la propriété collective comme contremodèle dans lequel les ménages acquièrent des parts sociales qui sont dissociées de la jouissance d'un logement en particulier. L'habitat participatif coopératif partage ainsi avec les organismes fonciers solidaires l'enjeu de soustraire des marchés spéculatifs les terrains et les logements destinés aux classes moyennes et populaires.

• Le vieillissement de la population et la recherche de solutions alternatives face à la faiblesse des offres proposées actuellement en termes de logement ou d'hébergement du troisième âge favorisent le recours à l'habitat participatif pour cette génération. Se développent des formes d'habitat fondées sur l'entraide et la solidarité entre personnes âgées et/ou avec des résidents d'autres tranches d'âge, tout en garantissant l'autonomie des individus et la possibilité d'un bien vieillir ensemble [START_REF] Labit | L'habitat participatif : une solution pour bien vieillir ? Note de synthèse réalisée pour la Fondation de France[END_REF].

• A divers titres, la question de l'habitat participatif s'inscrit largement dans le débat sur la fabrique de la ville durable et y prend une valeur d'exemplarité. Il valorise l'inscription des nouveaux logements dans des dynamiques locales, parfois en revitalisant un patrimoine bâti dont l'échelle ne permettrait ni l'acquisition par un particulier ni l'intervention d'un maître d'ouvrage professionnel public ou privé. Il s'inscrit dans des dynamiques de quartier et de revitalisation urbaine, privilégiant le voisinage et l'échange avec les associations présentes. Par sa capacité à mettre en réflexion un projet dans toute sa complexité, l'habitat participatif a souvent permis le réinvestissement de fonciers délaissés, incommodes. Il a également facilité la densification des tissus urbains existants, notamment dans les zones urbaines mixtes où subsistaient d'anciennes parcelles artisanales ou industrielles de petite taille. Enfin, les opérations d'habitat participatif s'inscrivent dans les exigences en matière de renouvellement du parc immobilier. Ces démarches ont une valeur démonstrative et répondent aux demandes de ménages recherchant un habitat plus « vertueux » sur le plan environnemental.

L'habitat participatif a sans doute joué un rôle dans le « réenchantement » de la participation dans le champ des questions urbaines après les critiques et phénomènes d'usure qu'avaient subis les dispositifs institutionnels de type conseils de quartier issus de la loi Vaillant de 2002 [START_REF] Nez | Urbanisme, la parole citoyenne[END_REF]. Il a mobilisé l'intérêt des acteurs publics nationaux et d'un grand nombre de collectivités territoriales qui ont apporté un soutien direct ou indirect à ce type de projets [START_REF] Devaux | Habitat participatif et politiques de l'habitat : un mariage par intérêt ?[END_REF]. Pour autant, et malgré une forme d'engouement médiatique autour de ce terme, la production reste quantitativement minime et l'ambition des militants de la première heure à imposer une troisième voie peine à trouver des relais. Au-delà du cas français, des mobilisations s'apparentant au co-housing interrogent les conditions du logement en Europe et invitent à en réinventer les règles du jeu, tant du point de vue de la gouvernance que des processus de production [START_REF] Bresson | How does collaborative housing address the issue of social inclusion? A French perspective[END_REF]. A ce titre, l'habitat participatif français agit comme un signal faible qui rappelle que la crise du logement ne peut être cantonnée à un problème de volume de production mais qu'elle porte aussi sur une exigence en termes de qualité de l'offre, tant du point de vue des espaces bâtis que des formes de voisinage. Face à la dérive des pratiques spéculatives du logement, des valeurs de solidarité et d'entraide cherchent à s'imposer comme de nouvelles ressources d'une économie domestique renouvelée.