# Multi-proxy documentation of past storm events in a mid-latitude coastal environment

Pierre Pouzet, Armelle Decaulne, Beatriz M. Funatsu

► To cite this version:

Pierre Pouzet, Armelle Decaulne, Beatriz M. Funatsu. storm events in a mid-latitude coastal environment. 10.1016/j.catena.2025.109118. hal-05078633

Multi-proxy documentation of past
CATENA, 2025, 257, pp.109118.

HAL Id: hal-05078633

https://hal.science/hal-05078633v1

Submitted on 22 May 2025

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# Multi-proxy documentation of past storm events in a mid-latitude coastal environment

Pierre Pouzet<sup>a,*</sup>, Armelle Decaulne<sup>b</sup>, Beatriz M. Funatsu<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Coastal environment

Extratropical storm

Historical archives

Dendrochronology

Meteorological reanalysis

Wind data

## ABSTRACT

As extreme events are expected to become more frequent, the combination of different environmental approaches is essential to apprehend the evolution of their occurrences. This requires a substantial time depth with a robust record of such events. This work presents a combined approach crossing historical, dendrochronological and meteorological indicators to document storm occurrences from a primary maritime pine (*Pinus pinaster*) tree-ring dataset extracted at Pointe d'Arcay coastal environment (western France), covering the period 1964 to 2019. From the analyses of tree-ring patterns, we identified fourteen years during which the growth of trees was strongly disturbed by significant storm winds, including those associated with Lothar and Martin (1999), Xynthia (2010), Joachim (2012), Klaus and Quinten (2009) storm systems. The analysis of wind parameters (intensity, direction, and persistence) based on ERA5 reanalysis showed a ~85 % fit between historical archives, dendrochronology, and meteorological data. As trees were sampled through the main tilting axis, tree rings revealed the impacts of windstorms originating from only one direction. The wind intensity appears to be an important factor to produce tree-ring eccentric growth; however, it does not explain all eccentric growth cases. Thus, we analyzed wind persistence as a potential parameter influencing dendrochronological signals recorded during years without storms. We demonstrate that dendrochronology, with the support of meteorological, historical and reanalysis wind data, accurately documents storm winds in a mid-latitude coastal environment. Tree-ring archives are effective to estimate windy phases of extratropical storms and can be used to understand past storm activity with trees older than the available meteorological records.

## 1. Introduction

The western coast of France is regularly subjected to high impact wind storms as it is located on the path of an important storm track (Libertado et al., 2011; Lozano et al., 2004; Koks and Haer, 2020). Extreme events often compromise infrastructure and may cause loss of life (Lamb and Frydendahl, 1991; Deroche et al., 2014; Mornet et al., 2015; Pirret et al., 2017). For instance, the combined financial loss due to the Lothar and Xynthia events, respectively in 1999 and 2010, reached over 10 billion dollars (Mayer et al., 2005; Widmer et al., 2004; Liberato et al., 2013; Mornet et al., 2015). Such extremes are expected to be more frequent due to climate change (Stott et al., 2016; Masson-Delmotte et al., 2021); however, detecting systematic changes in their occurrence given the relative shortness of observational records remains difficult to establish.

Windstorms also affect forest dynamics and disturb their associated

ecosystem services (Trouet 2020; Widmer et al., 2004; Zielonka et al., 2010; Imbert 2018). For example, during Lothar and Martin storms in December 1999, a total of 26.1 million m³ of wood was downed in the Landes region (SW France; Cucchi and Bert, 2003). Detecting past meteorological hazards through dendrochronological approach has been mainly centered on tree-ring isotopes studies (mainly δ¹⁸O) to expose the precipitation excess or deficit in tropical environment influenced by cyclonic activity (Berkelhammer and Stott, 2008; Brienen et al., 2012; Miller et al., 2006; Imbert 2018; Trouet et al., 2016; Tucker et al., 2018). While several studies use dendrogeomorphological analysis to investigate fluvial and mass-movement processes (Stoffel and Bollschweiler, 2008), others have exposed a relation between ice storms and tree-growth disturbances at temperate latitudes (Meentemeyer, 1991 in the Appalachian Piedmont; Lafon and Speer, 2002 in Virginia; Olthof et al., 2003 in southern Ontario).

Tree-ring analyses have been used for climatic studies regarding high

<footnote><sup>a</sup> Univ Angers, Nantes Université, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG UMR 6112, 49000 Angers, France</footnote>

<footnote><sup>b</sup> CNRS, UMR 6554 LETG, Nantes Université, France</footnote>

<footnote>* Corresponding author at: 2 Boulevard Lavoisier, 49045 Angers Cedex 1, France.</footnote>

<footnote>E-mail address: pierre.pouzet@univ-angers.fr (P. Pouzet).</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Location of Pointe d'Arçay area, western France and *Pinus Pinaster* stand on the sand dune, with the SHOM/IGN Litto3D digital elevation map, and a photography taken on the western edge of the stand.</figcaption></figure>

impact meteorological events, such as dating drought events (Flower et al., 2014; Linderholm and Molin, 2005; Ljungqvist et al., 2020), tracing and quantifying the frequency of historical storm tracks (Wise and Dannenberg, 2017), understanding the ecological role of high wind events (Hadley and Knapp, 2011), estimating storm activity (Johnson and Young, 1992; Knapp and Hadley, 2012) and their signatures on the variations in tree-ring growth (Hadley and Knapp, 2011; Fang et al., 2022). However, despite the demonstrated usefulness of tree-ring analyses, dendrochronological responses do not correlate linearly to every storm, thus indicating that the impact of wind forcing on tree-ring responses may be nuanced by wind parameters other than intensity.

The present study examines this hypothesis by using a combination of approaches in an area where official, long-term and reliable registers are available. We aim at both qualifying and quantifying uncertainties and strengths of tree-ring analyses in recording occurrences of extra-tropical storms. We propose a multi-source investigation based on written archives, tree-ring, and meteorological analyses to comprehend the past evolution of storm occurrences and to distinguish the influence of different wind parameters (wind direction, intensity and/or persistence) on the tree-growth patterns.

## 2. Study site

The study site consists in a tree stand located on Pointe d'Arçay dune (46.310641°N; 1.311625°W, Fig. 1), in western France. The site is located on a prograding sand spit in a dynamic coastal environment (Poirier et al., 2017). Significant morphological mutations were documented in this area (Chaumillon et al., 2004; Chaumillon and Weber,

2006). The sandy spit developed after the 17th century (Pouzet et al., 2021), with sediment gain reaching 5500 m² per month since 1987 in its southern part (Allard et al., 2008). The tree stand plays a key role in the dune preservation, as the sand spit runs along a low elevation estuary (Fig. 1). The stand, within a 12 ha maritime pine (*Pinus pinaster*) forest, is selected as numerous trees were already visible on the oldest available aerial photographs taken in 1945, on a sand dune depraved of surficial morphologic accidents. The State-owned protected forest is in the southern Pays de la Loire region, in the city of L'Aiguillon-la-Presqu'île, gathering the former La-Faute-sur-Mer and L'Aiguillon-sur-Mer towns since 2022. The closest synoptic Météo-France station is in La Rochelle, approximately 19 km south-east of the study site. The climate type is Marine West Coast according to the Köppen classification, with mean monthly temperatures ranging from 6.5 °C to 20.1 °C, mean annual precipitation of 744 mm, and mean monthly average 10-m wind intensity of 14.4 to 18 km/h (2000-2010 baseline). In addition to strong winds, marine flooding may also affect the site during a storm, as during the 2010 Xynthia event (Feuillet et al., 2012; Mercier et al., 2019).

## 3. Material and methods

### 3.1. Building up a database to develop the storm chronology from written sources

A database with an inventory of storms was compiled from the Météo-France (official meteorology and climatology service in France) directory and crossed with written archives. We first focused on storms with associated winds within a radius of 100 km around Pointe d'Arcay,
as storm or hurricane intense-wind radius can reach nearly 75 km (Irish et al., 2008). Wind impacts generally appear in the written sources as uprooted trees, collapsed church towers, crane falls, destroyed forests, damaged dams or/and boats, blown off roofs or damaged coastal infrastructures. A qualitative appraisal of impact intensity was defined according to the following: (i) impact is qualified as “significant” when at least one storm, recorded in the archives, caused at least one wind-related impact at the site within 100 km; (ii) impact is qualified as “light” when at least one wind impact is recorded within the 100 km radius around the study site, but without wind speed strong enough to be qualified as storm.

### 3.1.1. Météo-France archives

The chronology of storm reports and associated meteorological parameters (pressure, wind, and precipitation) of recent events was built based on data available upon request at the Météo-France Storm Report website (https://tempetes.meteo.fr/). This dataset, extending back to 1703, provides information about impacting storms only; minor events that could have been nevertheless locally severe are not listed if damage was not reported.

### 3.1.2. Historical archives

Local written archives and other digital sources are combined to accurately extend the storm chronology in the study area and to specify local impacts of minor events. Historical documents were consulted from public libraries, archives, and narrative sources (chronicles, diaries, essays, etc.). These documents contain observational and descriptive data on past extreme weather occurrences. Before being used to reconstruct the storm chronologies over the last decades, their reliability was evaluated based on the witness statement by the archive author and the institutional framework of the record (Athimon et al., 2016; Athimon and Maanan, 2018; Pouzet and Maanan, 2020; Nazzareno et al., 2021).

## 3.2. Dendrochronology

### 3.2.1. Core sampling and laboratory analyses

Dendrochronological analyses were carried out from samples taken from a *Pinus pinaster* (Maritime pine) stand on the outer fringe of Pointe d'Arcay dunes (Fig. 1). A total of 30 *Pinus pinaster* was sampled with a Mattson™ corer (5.15 mm diameter) following classical dendrochronological methods (Grissino-Mayer, 2003), and their locations were collected with a standard Global Positioning System (GPS). We selected trees presenting eastwards tilting and located on the western fringe of the forest, i.e., the outer side of the stand closer to the sea exposed to dominant western winds. In addition, ten cores were extracted from five trees showing a straight morphology of the stem, apparently not affected by winds, to serve as growth references, a few hundred meters within the stand. Sampling was carried out at breast height, throughout the entire stem, to collect increment cores from both the windward and leeward sides of the tilted stems. Comparison of tree-ring widths from both sides highlights asymmetrical/eccentric growth, as the tilting of the tree generates compression wood production on the leeward side of the stem concomitant with a growth drop in the same ring on the windward side. Following dendrochronological preparation guidelines (Stokes and Smiley, 1968; Grissino-Mayer, 1995; Speer, 2012), samples were stored just after extraction within labelled protective plastic pipes; then, in the lab, samples were dried at room temperature over two weeks inside the protective pipes. When dry, samples were mounted on wood supports and sanded with a 400-grit electric belt sander. We then performed ring-width measurements on a Lintab Rinntech™ platform. The Tsap-Win™ software enabled tree-ring width measurements; then measurements were cross-checked at the scales of the tree (two opposite axes) and at the scale of the stand to provide a robust chronology.

### 3.2.2. Storm occurrence assessed by tree rings

To fit the tree-growth pattern with the yearly record of storms, successive dormancy and growth seasons were considered as one year, i.e., November to February as the dormancy season and March to October as the growth season. Any disturbance (e.g., tilting undergone during an excessive wind event) experienced by a tree during its dormancy phase is revealed in the following tree-ring formation over the next growth season. An entire year in this chronology starts November of the year n-1 to end October of the year n. Historical storms in Pointe d'Arcay usually occur during winter, with their dendrochronological impact then recorded in the following growth season. During the growth season, the production of cambial cells is regulated along the tree ring to counterbalance the tilting-stress encountered during the dormancy period by strong winds, forming compression wood on the stems of *Pinus pinaster*. Compression wood is visible as an asymmetric growth, due to increased lignin production in cells on the leeward side of the wind impact, i.e., thicker leeward ring compared to the same ring on the windward side of the stem.

To detect wind disturbance imprints on tree growth, a comparison is made between tree-ring widths (TRW) of asymmetric radiuses C (radius where the wind comes from, i.e., windward) and D (radius where the wind goes to, i.e., leeward) of all trees sampled. Then, a year of growth disturbance attributed to windstorm is identified when an increasing growth value in D is concomitant to a decreasing growth value in C. In the case of successive disturbed years, only the first year of disturbance is considered, as the tree might react to a disturbance for several years (Schweingruber, 1996). An identified year is recognized as significant when at least two trees react to wind pressure, during a year with a minimum of seven living trees (representing 25 % of the entire sample depth, the first quartile has been selected as threshold to ensure robustness). The chronology therefore starts in 1964.

For each year of the chronology, an index of storm disturbance (ISDn) is calculated to evaluate the occurrence of one stormy period (either one single storm or a series of storms, as the tree-ring seasonal response does not make a distinction between events within the same year, Pouzet et al. (2018), derived from Schroder (1978), studying slope movements), as follows:

$$ISD(n) = (DT(n)*100)/LT(n) \quad (1)$$

where ISD(n) is the Index of Storm Disturbance (in percent) of the year n; DT(n) is the number of disturbed trees during the year n; LT(n) is the number of living trees in the year n; the higher the value the stronger the storm impact on the stand investigated.

The storm return periods are also calculated for each of the analyzed samples. The Storm return period (SRPt) represents the average time in year between storm events over the tree-life scale, calculated as follows:

$$SRPt = GDt/YRt \quad (2)$$

where GD represents the amount of growth disturbances for each tree and YR the total number of years tree t was alive.

## 3.3. Crossing dendrochronology responses with the historical storm chronology

For each year, “compliance”, “non-compliance” or “uncertainty” between the storm chronology and the dendrochronological responses were considered, following the reasoning below:

### (i) Compliance:

- A year with at least one storm with significant impacts inventoried in the area combined with more than one tree disturbed in the dendrochronological sequence; or

- A year linked to a light storm, combined with a slight dendrochronological signature (ISDn < 66th percentile of the series); or
<figure><img src="image_4.png" /><figcaption>Fig. 2. Impacting storms in the area compiled from Météo-France database and written archives.</figcaption></figure>

- No event recorded in both written sources and tree rings.

(ii) Non-compliance:

- A year without any storm recorded in archives, however, with a significant dendrochronological impact (ISDn > 66th percentile, that is, in the upper third-highest impact); or

- A year with at least one high-impact storm inventoried in the area, however, without any dendrochronological signature.

(iii) Every other combination is considered as an uncertainty. These cases are linked either to the lack of record of significant wind impact, or to a weak dendrochronological signature. In this case, ERA5 local reanalysis data are used to assess the uncertainty.

### 3.4. ERA5 reanalysis

Reanalysis consists in numerical descriptions of the ocean-atmosphere climate system combining models and observations, and is used here as ancillary data to compensate missing meteorological information in written archives. Reanalysis gives the opportunity to investigate the links between the identified disturbed tree rings and the historical storm chronologies, as it consists of temporally consistent and continuous data. ERA5 (Hersbach et al., 2020), one of the latest generations of reanalysis, provides hourly estimates of oceanic and atmospheric parameters with global coverage and spatial resolution of 0.25° latitude × 0.25° longitude. The nearest grid point to the site is centered at 46.25°N–1.25°W (located fifteen kilometers north-east to the study site); at this latitude, the spatial resolution corresponds to an area of approximately 21 × 27.8 km.

We used ERA5 10-m surface wind and instantaneous wind gust for the 1980–2019 period; we consider the period after 1979 as it corresponds to the period in that satellite data has been consistently assimilated. The instantaneous wind gust is defined as the maximum 3 s wind velocity at 10 m height; the 3 s gust is computed every time step of the model run and the maximum value is kept since the previous post-processing (see Bech and Bidlot, 2009; ECMWF, 2021 for details). ERA5 data have been shown to overestimate mean zonal winds (i.e., winds along the local parallel of latitude) and underestimate poleward winds in the mid-latitudes (Rivas and Stoffelen, 2019), compared to the Advanced Scatterometer (ASCAT) ocean vector wind measurements. ERA5 has better performance than ERA-INTERIM in terms of mean and transient wind errors, wind divergence and wind stress curl biases (Rivas and Stoffelen, 2019; Minola et al., 2020). In the lack of in-situ wind and wind gust measurements, reanalysis data represent the only alternative to consistently analyze wind conditions affecting areas where ground station data are unavailable. The present study offers an opportunity to evaluate ERA5 as to its effectiveness to detect stormy conditions through comparisons with historical records and dendrochronological signatures. Its potential to identify storms that are strong enough to affect tree growth is also evaluated.

### 3.5. Wind data as a tool to reduce storm occurrence uncertainties

The wind gust records have been extracted from the ERA5 hourly record for each storm that impacted the area since 1980. The wind direction (that is, the direction from which the wind blows) was then compared with the sampling direction to estimate the probability of detecting each storm in the dendrochronological sequence. Before 1980,

the most intense wind gust recorded per storm and the mean wind direction were recovered from the historical archives. Furthermore, we retained the ten most intense wind gust records per year and calculated their mean value (G10) while retaining the associated mean wind direction for comparisons against the tree ring sampling direction.

Finally, the persistence of strong winds (Beaufort Scale (Bf) ≥ 6, corresponding to wind speeds above 40 km/h), whether or not associated with a storm, is a parameter also investigated with three different indicators: (i) the accumulated hours of strong wind; (ii) the number of days with strong wind, and (iii) the number of consecutive days with strong wind for at least two consecutive hours, per year. An “overall compliance” between dendrochronological archives and historical records with the support of reanalysis data is then estimated to specify cases with uncertainties.

## 4. Results

### 4.1. Historical storm chronology

From the Météo-France database and the analysis of impacts recorded in historical archives, 44 storms are inventoried between November 1st, 1963 and October 31, 2019 in the historical storm chronology (Fig. 2; Table 1). As detailed in Table 1, among these 44 storms, 12 were classified as “storm with light impact on the studied area”, and 32 events produced significant impacts, thus classified as “significant storm impacting the area”. The highest number of storms is recorded in 1998, with four occurrences, then 1990 and 2000 (3), 1987, 2009, 2014 and 2018 (2). Data show that 82 % (36) of storms recorded over the period November 1964–October 2019 occurred during the dormant season (November, December, January and February months).

### 4.2. Tree-ring growth disturbances and storm chronology

#### 4.2.1. Building the tree-ring chronology

ERA5-based wind analysis shows that breeze with wind speeds up to ~20 km/h from the northern directions are ubiquitous throughout the year but strong winds (>46 km/h) are mostly from the NW to SW quadrant (Fig. 3A). This distribution of strong winds is largely influenced by the prevailing strong winds from November to February, mostly from the WSW, SW and W (Fig. 3B). Tree tilting conforms to main wind direction on the outer fringe of Pointe d'Arçay as they are mainly leaning from W to E, with several samples impacted by NW winds (Fig. 4A). Trees ranged from 30 to 70 years old in 2019 (Fig. 4B), the older trees tend to be located on the eastern part of the sampling area. Fig. 4C shows the variation of the tree diameter (without bark), measured between 200 and 400 mm.

The tree reference growth curve is presented in Fig. 5A, over the period 1964–2019; impacted trees present a distinct growth from the reference one, as illustrated with trees #20 (Fig. 5B) and #16 (Fig. 5C), which recorded respectively six and eight growth disturbance (Fig. 6). From the 30 trees sampled, 27 present readable cores enabling building up a robust chronology; three samples show no clearly identified boundary between latewood and the next earlywood.

#### 4.2.2. Storm detection using dendrochronology

At the scale of a tree, one of the most significant cases highlighted in
<caption>**Table 1**</caption>

Meteorological parameters of storm inventoried from reanalysis data and historical archives information (nrd*: no reanalysis data); and definition of a significant or light wind impact for each stormy year. Damages and bibliographic references are presented in Supplementary Material 1.

<table><thead><tr><th rowspan="2">Year<br>(from Nov.<br>N-1 to Oct.<br>N)</th><th colspan="2">STORM ARCHIVES</th><th colspan="2">STORM METEOROLOGICAL PARAMETERS FROM<br>REANALYSIS</th></tr><tr><th>Number<br>of storm</th><th>Storm dates<br>(month/day/year)<br>with name</th><th>Highest wind gust record on<br>reanalysis (since 1979) /<br>archives (before 1979) per<br>storm<br>km/h (Beaufort sc.)<br>MM/DD/YY/HH(UTC). M (1980-<br>2019) = 89,3</th><th>Main wind direction (mean<br>ERA5 wind) on<br>reanalysis (since 1979) /<br>archives (before 1979) per<br>storm<br>Example : W -> from W to E</th></tr></thead><tbody><tr><td>2019</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2018</td><td>2</td><td>1/3/18: Eleanor<br>1/1/18: Carmen</td><td>77km/h (9) 1/3/18/15<br>102.4km/h (10) 1/1/18/2</td><td>W<br>W</td></tr><tr><td>2017</td><td>1</td><td>3/6/17: Zeus</td><td>99.1km/h (10) 3/6/17/11</td><td>WNW</td></tr><tr><td>2016</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2015</td><td>1</td><td>11/3/14: Qendresa</td><td>72.6km/h (8) 11/3/14/14</td><td>SW</td></tr><tr><td>2014</td><td>2</td><td>2/14/14: Petra<br>12/23/13: Dirk or "The<br>tempête de Noël"</td><td>80.3km/h (9) 2/13/14/12<br>76km/h (9) 12/24/13/10</td><td>W<br>SW</td></tr><tr><td>2013</td><td>1</td><td>10/27/13: Christian</td><td>80.2km/h (9) 10/28/13/6</td><td>SW</td></tr><tr><td>2012</td><td>1</td><td>12/16/11: Joachim</td><td>111km/h (11) 12/16/11/7</td><td>WSW</td></tr><tr><td>2011</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2010</td><td>1</td><td>2/27/10: Xynthia</td><td>123.4km/h (12) 2/28/10/3</td><td>WSW</td></tr><tr><td>2009</td><td>2</td><td>2/9/09: Quinten<br>1/24/09: Klaus</td><td>108.7km/h (11) 2/10/09/0<br>88.6km/h (9) 1/24/09/3</td><td>WSW<br>SW</td></tr><tr><td>2008</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2007</td><td>1</td><td>12/8/06: Véra</td><td>86.8km/h (9) 8/12/06/6</td><td>SW</td></tr><tr><td>2006</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2005</td><td>1</td><td>12/17/04: Dagmar</td><td>84.8km/h (9) 12/17/04/13</td><td>W</td></tr><tr><td>2004</td><td>1</td><td>1/12/04: Gerda</td><td>93.9km/h (10) 1/13/04/7</td><td>WSW</td></tr><tr><td>2003</td><td>1</td><td>1/2/03: Calvann</td><td>97.9km/k (10) 1/2/03/4</td><td>SW</td></tr><tr><td>2002</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2001</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2000</td><td>3</td><td>10/30/00: Oratia<br>12/27/99: Martin<br>12/26/99: Lothar</td><td>89.8km/h (10) 10/30/00/11<br>140.9km/h (12) 12/27/99/20<br>113km/h (11) 12/26/99/5</td><td>SW<br>WNW<br>WSW</td></tr><tr><td>1999</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1998</td><td>4</td><td>1/13/98<br>1/5/98<br>1/4/98<br>1/2/98</td><td>88.1km/h (9) 1/13/98/9<br>83km/h (9) 1/5/98/7<br>85.6km/h (9) 1/4/98/8<br>97km/h (10) 1/2/98/1</td><td>SW<br>SW<br>SW<br>WSW</td></tr><tr><td>1997</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1996</td><td>1</td><td>2/7/96</td><td>116.4km/h (11) 2/7/96/17</td><td>W</td></tr><tr><td>1995</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1994</td><td>1</td><td>1/5/94</td><td>95.2km/h (10) 1/5/94/2</td><td>SW</td></tr><tr><td>1993</td><td>1</td><td>9/12/93</td><td>100.3km/h (10) 9/13/93/7</td><td>SW</td></tr><tr><td>1992</td><td>1</td><td>12/18/91</td><td>76.7km/h (9) 12/19/91/20</td><td>WSW</td></tr><tr><td>1991</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1990</td><td>3</td><td>2/26/90: Vivian / Wiebke<br>2/3/90: Herta<br>1/25/90: Daria</td><td>93.3km/h (10) 2/27/90/7<br>106.9km/h (11) 2/3/90/10<br>96.1km/h (10) 1/25/90/5</td><td>W<br>SW<br>WSW</td></tr><tr><td>1989</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1988</td><td>1</td><td>1/22/88</td><td>73.7km/h (8) 1/22/88/1</td><td>SW</td></tr><tr><td>1987</td><td>2</td><td>10/15/87: "L'Ouragan" or<br>"The Great Storm"<br>6/7/87</td><td>95.5km/h (10) 10/15/87/22<br>70.7km/h (8) 6/7/87/18</td><td>SW<br>WNW</td></tr><tr><td>1986</td><td>1</td><td>3/24/86</td><td>97.5km/h (10) 3/24/86/16</td><td>W</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 1 (continued)</caption><thead><tr><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>1985</td><td>1</td><td>11/22/84</td><td>86.7km/h (9) 11/22/84/22</td><td>SW</td></tr><tr><td>1984</td><td>1</td><td>1/23/84</td><td>83.4km/h (9) 1/23/84/21</td><td>WNW</td></tr><tr><td>1983</td><td>1</td><td>11/6/82</td><td>89.1km/h (10) 11/8/82/9</td><td>SW</td></tr><tr><td>1982</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1981</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1980</td><td>1</td><td>12/15/79</td><td>98.4km/h (10) 12/15/79/0</td><td>SW</td></tr><tr><td>1979*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1978*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1977*</td><td>1</td><td>12/2/76</td><td>Local wind gust estimated (Météo-France): Pointe de Chassiron 133 km/h, 12/1/76 (143km south of Pt.A).</td><td>NW: article 168 of the Météo-France Storm Report website http://tempetes.meteo.fr/</td></tr><tr><td>1976*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1975*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1974*</td><td>1</td><td>2/6/74</td><td>Local wind gust estimated (Historical archive): 185 km/h (France, no location, no time, high uncertainty).</td><td>W</td></tr><tr><td>1973*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1972*</td><td>1</td><td>2/13/72</td><td>Local wind gust estimated (Météo-France): La Rochelle 140 km/h, 2/13/72 (19km south-east of Pt.A).</td><td>NW: article 166 of the Météo-France Storm Report website http://tempetes.meteo.fr/</td></tr><tr><td>1971*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1970*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1969*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1968*</td><td>1</td><td>1/7/68</td><td>Local wind gust estimated (Météo-France): Poitiers, 120km/h, 1/7/68 (130km east of Pt.A).</td><td>W: article 163 of the Météo-France Storm Report website http://tempetes.meteo.fr/</td></tr><tr><td>1967*</td><td>1</td><td>3/12/67</td><td>Local wind gust estimated (Météo-France): Île d'Yeu, 148km/h, 3/12/67 (92km north west of Pt.A).</td><td>W: article 97 of the Météo-France Storm Report website http://tempetes.meteo.fr/</td></tr><tr><td>1966*</td><td>1</td><td>6/22/66</td><td>No local wind gust estimated in any sources. Qualitative data from historical archives: many trees uprooted/broken: park of the great blotterreau, boulevard augusta pineau, trees uprooted park of Noë-Mitrie, falling tree on house, broken roofs, 26 cars blown away, a bell tower falling in Doulon (Nantes, 120km north of Pt.A)</td><td>No wind directions estimated in any sources.</td></tr><tr><td>1965*</td><td>0</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1964*</td><td>1</td><td>11/1/63</td><td>No local wind gust estimated in any sources. Qualitative data from historical archives: The main damage recorded are dikes broken by the sea in central western France, no wind due damages mentioned but high winds may have occurred.</td><td>No wind directions estimated in any sources.</td></tr></tbody></table>

the Fig. 5C is the 1999–2000 evolution recorded by sample #16, which induced a leeward radius growing from 1.98 to 4.11 mm (then nearly 9 mm the following year) and a windward radius decreasing from 1.93 to 1.04 mm (Fig. 5C). It confirms the disturbance (tree-ring eccentricity growth, or TREG) recorded by sample #16 for 2000 (Fig. 6).

At the scale of the entire stand, numerous growth disturbances have been recorded in tree rings from 1964 to 2019 (Fig. 6), while the sample depth strengthen over years, to reach a steady number of 27 from 1986 (Fig. 7A). Over the 56 years covered by the dendrochronology, 19 present strong evidence of growth disturbances in at least two trees (Fig. 7A). In addition, 21 years show evidence of more than one tree impacted, however with an ISDn < 14.4 %. According to these dendrochronological results, a mean return period of strong winds having impacts on the tree-ring patterns is estimated to 10.16 years at the study site.

With an ISDn of 37 %, 2000 is the year recording most impacts, with ten trees strongly reacting with an eccentric growth visible within the tree ring-geometry (Fig. 7A). Another significantly impacted year is

2011, with nearly 33 % (9 reacting trees) of the samples showing clear tree-ring responses. The detailed ISDn results, ordering years according to their wind impact signatures, are presented in Fig. 8.

### 4.3. Compliance between the dendrochronology and written sources

We documented that 15 of the 19 (79 %) tree-ring highly impacted years (ISDn > 14.4 %) match at least one known storm passing through the study area and recorded in archives. Among the 56 years of the study chronology, the compliance between the historical storm chronology (without reanalysis) and tree-ring records is estimated to 64.3 % (36 years) (Fig. 9A, full data available in the Supplementary Material 1). We also reported 13 years (23.2 %) of uncertainties due to either a lack of precise records of wind impacts in the study area (1988, 2003, 2005) or to several years undergoing a weak dendrochronological signature (1975, 1979, 1982, 1991, 1995, 1997, 1999, 2001, 2002, 2006 and 2008). Seven other years (12.5 %) show no agreement between dendrochronological results and storm reports: 1972, 1990, and 2017
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Yearly (A) and winter months (B, November-February) climatology over the period 1979–2019 of 10-m wind at 46.25°N, –1.25°E, based on ERA5 hourly data.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Descriptive results of the stand based on the extracted data. A: Tilting direction; B: Tree age at sampling height in October 2019; C: tree diameter without bark.</figcaption></figure>

encountered impacting storms but no tree-ring disturbance, whereas 1971, 1981, 2011, and 2019 shows tree-ring disturbances when no storm was reported.

## 4.4. Meteorological parameters of storms from reanalysis data

ERA5 wind data are congruent with the intense storms inventoried by meteorological and historical archives (Table 1). All storms appear in the historical records with wind gusts over 70 km/h (8 Bf). Data also show that wind storms are predominantly westerlies, with a high variability from NW to SW, corresponding to the tilting direction of trees on the stand studied. The two most impacting storms were the well-documented Martin and Xynthia storms. Both storms had wind gusts over 118 km/h (12 Bf) and produced significant damage across western Europe (Ulbrich et al., 2001). Herta, Lothar, Quinten, and Joachim

storms also produced significant wind gusts of 100 km/h and above (11 Bf) with significant impacts (Schmoeckel and Kottmeier, 2008; Deroche et al., 2014; Pirret et al., 2017; Zschenderlein et al., 2019). Older storms, on March 12, 1967, Feb. 13, 1972 and Dec. 2, 1976 may have also produced intense wind gusts; however, for those last three storms, historical sources do not provide accurate information.

Fig. 7B shows the highest wind gust records (G10) per year from the ERA5 reanalysis. Gusts of 116.2 km/h were recorded in 2000, linked with the intense storms Lothar and Martin. Years with the strongest wind gusts correspond to those with at least one significant storm impacting the area (Table 1). The predominant wind directions were WSW, W and SW, in agreement with the winter wind regime (Fig. 3B) and tree tilting direction (Fig. 4A).

Years with storms recorded are not necessarily those with the longest strong wind persistence (Fig. 7B). Years that were the most subjected to
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Tree-ring widths from undisturbed reference and impacted trees. A: Dendrochronological reference curve for the stand (mean width of the four radius A, B, C and D, C-D being the direction of the stem tilting). B and C: Dendrochronological curves of two impacted trees sampled (B: tree #20 and C: tree #16) with the windward (red line) and the leeward (green line) radius; and the growth disturbances recorded (dashed lines). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

strong (>6 Bf), persistent winds (hourly and daily) are, in decreasing order, 2001, 2007, 2014, 2008, 2004, 1988, 2018, 2010, 1995, 2012, 2013, 2016, 1994 and 1990. During 2001, 2008, 1995 and 2016, no storm was recorded in either meteorological and historical archives, whereas 2007, 2004, 1988 and 2013 were subjected to only one storm with a light wind impact on the study site (Table 1). Wind persistence is not consistently linked to storm occurrences or high G10, however it may explain some of the weaker TREG detected in the dendrochronological sequence (2016, 2013, 2008, 2006, 2002, 2001, 1995, 1988, 1984 and 1980, more details are available in Supplementary Material 1).

### 4.5. Using local reanalysis data to link historical and dendrochronological storm chronologies

#### 4.5.1. Highlighting the compliances between historical data and the tree ring chronology

For each year of compliance showing a dendrochronological signature of a storm, reanalysis data also highlighted high impacting winds. The best example of this occurred in 2000, with the strongest dendrochronological impact (ISDn = 37 %) coinciding with a succession of

severe storms from October 1999 to February 2000, including Lothar and Martin, which yielded wind gusts above 140 km/h, intense and persistent westerlies (Fig. 7B). Years 2018 and 2014 (more than two storms in each year), 2012 (Joachim), 2010 (Xynthia), 2009 (Klaus and Quinten), 1998 (four different storms) and 1986 (one storm) are also good examples of the compliance between storm occurrence and dendrochronological response.

#### 4.5.2. Explaining the uncertainties between historical data and the tree ring chronology

Reanalysis data were used to understand the apparent absence of agreement between tree-ring analyses and historical data for the twelve uncertain years (Fig. 9A). For example, in 2003 a TREG signature was not found despite a storm event being registered. The analysis of such cases showed that these uncertainties arise due to one or more of the following reasons: (i) non-exceptional wind strength of the storm (Fig. 7B), (ii) weak persistence of the strong winds (Fig. 7B), and/or (iii) the prevalence of south-westerly winds with which the coring direction of the stems was misaligned (Table 1).

Slight dendrochronological impacts were recorded in 2008, 2006,
<figure><img src="image_8.png" /><figcaption>Fig. 6. Disturbances recorded per tree sampled.</figcaption></figure>

2002, 2001, 1999, 1997, 1995, 1991 and 1982, during which any storms were recorded (Fig. 2; Fig. 7A). 2008, 2006, 2002, 2001, 1995 experienced significant wind persistence (Fig. 7B) potentially explaining the formation of wood reaction even though storm conditions were absent. *Pinus pinaster* trees are particularly sensitive to the persistence of strong winds, impacting their tree-ring growth (Wade and Hewson, 1979). For these five years, some impacted samples are particularly impacted (Fig. 6) and are tilted from the NW to the SE (Fig. 4E) which is the direction of strong yearly winds (Fig. 3A), but not necessarily winterly stormy winds (Fig. 3B). In 1999, 1997, 1991 and 1982, only two trees produced reaction wood (ISDn < 8 %, Fig. 7A and Fig. 7B). As no intense or persistent wind was recorded (Fig. 7B), these slight disturbances may not be linked to wind parameters and storm occurrences. As the ISDn is low for these four cases, it confirms the opposite hypotheses of a link between storm conditions and significant TREG at the entire stand scale.

Finally, a light storm impacted the area in 2005 while a significant dendrochronological impact (ISDn = 14.8 %) was found, pointing to an incongruity between dendrochronology and archives. As the ERA5 wind presents weak wind speeds and no persistence (Fig. 7B), 2005 remains uncertain.

### 4.5.3. Understanding the non-compliances between historical data and the tree ring chronology

Non-compliances between dendrochronology and historical storms may also be explained by reanalysis. For instance, during 2017, no severe storm signature was found in tree-ring patterns but did in 2018; during 2011, a tree-ring signature is recorded during a non-stormy and non-windy year (Fig. 7A, B). We hypothesize that these two mismatches are due to the persistence of wood reaction over several seasons due to the strong impacts of severe events occurring in the late storm season, recorded in the end of February 2010 (Xynthia) and in March 2017 (Zeus). This hypothesis is in accordance with the work of Stevenson et al., (2016) performed in the Ouachita Mountains (USA) indicating that severe winter storms can leave a characteristic decline in ring width also in the following years.

Finally, the absence of compliance in 2019, 1990, 1981, 1972 and 1971 remains unexplained. We may infer that the 2019 and 1981 TREG

might also be due to the strong storms that impacted the area the year before, in 2018 and 1980. Fig. 7B shows that 2018 presented persistent and strong winds; and that 1980 had intense wind gusts during the 1979 December storm. For 1990, the absence of TREG during a stormy and windy year remains unexplained. We hypothesize that this non-compliance might be partially due to the coring direction as storm winds were from the SW quadrant. No hypotheses can be proposed for the 1972 and 1971 cases as they precede the ERA5 period used in this study.

## 5. Discussion

### 5.1. Overall compliance between the three data sets and regional storm observations

Results of this study indicate an overall compliance between tree-ring records, historical storm chronology and reanalysis data to document historical coastal storms. After using reanalysis to explain uncertainties between historical sources and the tree ring chronology (Fig. 9B), the overall compliance between the dendrochronological sequence and every meteorological data (the historical storm chronology crossed with reanalysis and meteorological data) since 1964 (56 years) is estimated at 82.2 % (46). Five years remain uncertain (8.9 %), and five others are still noncompliant (8.9 %). During the timeframe of the reanalysis data, the compliance since 1980 (40 years) is 85 % (34 years), with 7.5 % uncertainty (three years) and 7.5 % non-compliance (three years). ERA5 reanalysis data appears as a useful tool contributing to the understanding of the dendrochronological responses to past storms, highlighting the link between storm occurrences and TREG. The details of the links between the dendrochronological results, storm occurrences and reanalysis data are presented for each year since 1964 (Supplementary Material 1).

In addition to the results regarding storm dating we propose in the present study in Pointe d'Arçay, the literature proposes several other regional highlights regarding geomorphological impacts of storms (Fig. 9C). For instance, the most impacted year of the tree-ring chronology (ISDn of 37 %) highlights the imprint of the 1999–2000 winter
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Crossing dendrochronological and weather reanalysis data. A. Dendrochronological results: The Index of Storm Disturbance (ISDn, left y-axis) highlights several years; the 1960s are less representative as the number of living trees (LTn, right y-axis, red line) is low. B. Weather reanalysis wind data: Mean of the strongest wind gust records (G10, left y-axis, mean = 89.4, histograms); and wind persistence indicators from ERA5 reanalysis data (right y-axis): Cumulated hours of strong wind* persistence** (mean = 197, dotted line), number of days with strong wind* persistence** (mean = 33, dashed line) and number of consecutive days with strong wind* persistence** (mean = 13, full line). * *Strong winds: at least Beaufort 6 from mean wind records.* ** Persistence: at least two consecutive hourly records in a day.* (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

storms Lothar and Martin, which is also recorded by back-barrier sedimentological archives in Brittany, with marine parameters statistical return period of 10 years and a significant wave height exceeding the 0.995 quantile of the timeseries (Pouzet and Idier, 2024). With an ISDn of 22 %, the storms of January 1998 have also induced a TREG in the Pointe de Pen Bron tree-ring series (Pouzet et al., 2018), 150 km NE of Pointe d'Arçay. Winter 2013–2014, recorded in Pointe d'Arçay tree rings (ISDn of 15 %), induced extensive beach and dune erosion in south-western England (Masselink et al., 2015). In Brittany, cliff-top storm deposit displacements were recorded in its western rocky coasts (Autret et al., 2016), whereas sandy beaches were submitted to a deep coastline retreat (Blaise et al., 2015). The intense 2010 Xynthia storm (ISDn of 15 %), which produced local coastal flooding over the coasts of France, induced significant morphogenic impacts with dune breaches recorded at the Belle Henriette lagoon, a few kilometers north to the Pointe d'Arcay spit (Chauveau et al., 2011). In southern Brittany, significant pocket beach erosion was estimated (Dubois et al., 2011). Much farther from the coastline in France, windthrows were also detected in the Vosges mountains (NE France) composed of pine and spruce forests (Haidu et al., 2019) from the use of satellite imagery. Such wind impacts are like those of the 2009 Klaus storm (ISDn of 15 %) in the Landes Forest in the southwest of France (Colin et al., 2010). In 1974, the storm that caused an ISDn of 13 % in the Pointe d'Arcay dune stand was also

recorded by a Brittany back-barrier washover chronology, with oce-
anological parameters exceeding a 100-years of return period (Pouzet
and Idier, 2024).

5.2. *Tree-ring disturbances spatial distribution*

Fig. 10 shows that growth disturbances directly proportional to the storm return period have heterogeneous spatial distribution within the stand. We considered three parameters that could help explain this distribution, namely, the location of the sampled tree within the stand, the age of the tree, and tree diameter (without bark). Trees closer to the shoreline are more exposed to coastal winds, whereas tree diameter could impact the tree resistance to wind force.

At the studied stand, younger sampled trees are those located closest
to the shoreline (correlation coefficient CC = 0.57, Figs. 11 and 4B,
Supplementary Material 2-E); this west-to-east age gradient follows the
building of the sand spit during the last centuries (Allard et al., 2008).
There is a slight negative correlation between the distance from the
shoreline and the tree diameter (CC = -0.4, Fig. 11, Supplementary
Material 2-C) consistent with Fig. 4C. This observation could be
explained by the fact that the area closest to the shoreline is less densely
covered with trees, enabling them to benefit from a larger quantity of
nutrients. However, there is no direct correlation between the tree age
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Index of Storm Disturbance (ISDn) results per year sorted from the most to the less impacted years, with the number of trees during the year. Red values highlight the first third impacted years (ISDn > 14.44); orange values are years with a lesser tree-ring response (ISDn < 14.44) but with still at least two trees disturbed; and yellow values are years with a ISDn only induced by one tree disturbed. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

and their diameter at the scale of the sampling area (CC = -0.16, Fig. 11 and Supplementary Material 2-A). In accordance with the observation made by Schweingruber (1996), the oldest trees are not necessarily the widest as the tree diameter also depends on environmental factors.

The number of disturbances is weakly correlated to the tree diameter (CC = 0.22, Fig. 11, Supplementary Material 2-B); and in the same way the tree age or the distance to the shore appears as slight explanatory factors, showing CC = 0.27 and CC = 0.36, respectively (Fig. 11, Supplementary Material 2-D and Supplementary Material 2-F). These weak and not significant associations may be exemplified with tree #30, which is located on the southern edge of the sampling area (Fig. 4) and is the most impacted tree (Fig. 6). It shows 8 response years over a 47-year period, providing a mean return period of impacting storms of 5.9 years. On the other hand, sample #18 seems to be less exposed in the center of the study area (Fig. 4) recording only two major disturbances during its 52-year lifespan (Figs. 6 and 11). As other examples of this discussion, some of the most strongly impacted samples #30, #29, #23, #21, #13,

#10, #3 and #2 (Fig. 10) are located at the western part of the stand. There are also younger trees, because the mean age of these eight highly impacted samples is 44.9 years, which is lower than the nineteen other less impacted trees (mean of 54.11 years). These impacted trees are also thicker (mean diameter of 324 mm) than the nineteen others less struck by high winds (mean diameter of 273 mm). In addition, several samples located in the center, or NE, or SE parts of the stand are less impacted and show longer return periods (trees #28, #27, #18, #19, #16, #7, #5, Fig. 10). They all are particularly old (>50 years) and thin (<297 mm) trees. This means that even if the number of disturbances per trees is not fully correlated to their physical parameters and distance from the shoreline, a significant number of younger but thicker trees located in the western edge of the sampling area are highly exposed with low storm return periods (Fig. 11). This result reflects the main trajectories of extratropical storms, from the southwest in southern France and from the west in northwest France (Lozano et al., 2004).

Moreover, due to the tree sampling strategy, only westwards storms would be recorded within the collected dendrochronological sequence. In the study area, the strongest winds in the winter period (November through February) are from the W and the SW (Fig. 3B). However, some storms recorded in the area over the period investigated had totally different wind directions, coming perpendicularly to the typical storm circulation, from the WNW (2013), the WSW (1990) or the S (1987) (Table 1 and Supplementary Material 1). This explains the fact that during these years only a slight impact (if any) was recorded in the tree-ring archives (Fig. 7A). These results confirm previous assumptions made by Stoffel and Bollschweiler (2008) regarding the coring direction as a key parameter influencing the entire analysis. The coring direction reveals the tree-ring chronology of storm in a limited radial range.

### 5.3. Benefits and limitations of this multi-proxy methodology for climatological reconstructions

Regarding climatological analyses, Stoffel and Bollschweiler (2008) mentioned that the study of “winter storms” from dendrochronological records would be a future research direction. Since this review, most dendrochronological studies have focused on overall implications of climate parameters influencing storm records. For instance, storm activity in northern hemisphere sites is connected with moisture changes related to the ENSO system (Wise and Dannenberg, 2017) or the Pacific Decadal Oscillation (Berkelhammer and Stott, 2008; D’Arrigo et al., 2015). These examples highlight large-scale climatic patterns and do not focus on microscale impacts of historical storms. In line with the observations of Hadley and Knapp (2011) regarding impacts of high-wind events in tree-ring records, this study presents an original microscale contribution introducing the way that past extratropical storm occurrences can be revealed from dendrochronological records, with the support of other historical and meteorological data.

Limitations of the present study concern the role that other factors

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Compliance between the dendrochronological and historical archives (A); with the support of reanalysis or meteorological data (B)*; and with other regional studies (C). Full information is available in Supplementary Material 1. *Before 1980 the overall compliance is based solely on the analysis of meteorological parameters extracted from archives.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Fig. 10. Map of the storm return period per tree.</figcaption></figure>

may play on TREG and that were not considered here, such as solar radiation/cloudiness, soil conditions, precipitation, wood diseases and contamination. However, such factors generally influence the entire growth of the stand, whereas tree tilting and the asymmetrical growth of

the annual rings are mostly a result of physical stress caused by storm winds in a coastal position presenting no morphologic changes on the sand dune surface: 300 m within the stand, no trunk deformation can be observed. Further TREG studies on other locations, using the synergistic data combining approach proposed in this study should contribute to solidify a general picture of the impact of wind storms in different coastal areas in temperate zones.

## 6. Conclusions

This study presents a combination of historical, dendrochronological and meteorological indicators to document storm occurrence in a coastline environment over the last decades. From the tree-ring widths eccentric patterns detected, we identified 14 years during which trees were strongly disturbed by significant storm winds since 1964, including Lothar and Martin (1999), Xynthia (2010), Joachim (2012), Klaus and Quinten (2009) storms. We observe that several younger but thicker trees located in the western edge of the stand may have undergone more wind impacts than the others, but no linear correlations were found between shoreline distance and number of disturbances. The selection of core sampling direction on stems is a key factor that influences the entire analysis: only disturbed growth patterns caused by winds impacting the trees in the same direction of the prevailing wind are recorded. Wind persistence was found to be an important parameter influencing the dendrochronological signals recorded during non-stormy years, as it is related to a slight growth eccentricity (ISDn < 8 %).

There is no strict correlation between the dendrochronological proxy built with tree-ring widths and the historical storm chronology. However, with qualitative and quantitative information combined (reanalysis data and historical records crossed with dendrochronology), this study showed that past storms induced a strong response on tree-ring widths with an overall compliancy estimated at 85 % since 1980. The present study demonstrates how a holistic approach reduced the uncertainties of building a storm chronology based solely on tree ring archives. In addition, the present study supports ERA5 hourly reanalysis as a useful tool to explain local wind impacts recorded with the tree-ring patterns, which can be missed in other storm archives.

<figure><img src="image_13.png" /><figcaption>Fig. 11. Correlation synthesis between the distance from shoreline and tree age, tree diameter without bark and number of disturbances at sample height; tree labels are provided. Full information is available in Supplementary Material 2.</figcaption></figure>
## 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.

## Acknowledgments

Analyses, operating costs, and field measurements were funded by the DREAL (Direction Régionale de l'Environnement, de l'Aménagement et du Logement) of the Pays de la Loire and the UAR CNRS 3281 OSUNA (Observatoire des Sciences de l'Univers de Nantes Atlantique) through the research program History of Coastal Risks (Observatoire Régional des Risques Côtiers, OR2C). We also thank the Office National des Forêts (ONF) and the Parc Naturel Régional (PNR) of the Marais poitevin for providing coring permission and the dune stand access; and the Mesocentre ESPRI of the French IPSL for permission to access ERA5 reanalysis data and perform computational analysis.

## Primary Sources of meteorological archives

– *Météo-France Storm Report website upon request* http://tempetes.meteo.fr/

Articles 268, 258, 226, 188, 85, 132, 186, 165, 120, 117, 116, 87, 149, 150, 89, 176, 128, 174, 109, 111, 110, 182, 217, 218, 90, 99, 129, 98, 257, 250, 255, 127, 224, 168, 166, 163, 97.

– **Nantes Municipal Archives of Nantes:**

26PRESSE245: 14/01/1998.

24PRES49: 07/02/1974.

24PRES6: 24/06/1966.

– **Departmental archives of Loire-Atlantique:**

PR 967 849: 15-16/02/2014.

PR 967 58: 02-03/11/1963.

– **Other newspaper websites:**

https://www.francebleu.fr/infos/climat-environnement/carm en-a-commence-a-souffler-fort-sur-la-loire-atlantique-et-la-vendee-151 4799841.

https://www.lemonde.fr/planete/article/2013/10/27/tempete -12-departements-du-nord-ouest-en-alerte-orange_3503693_3244.html.

## Appendix A. Supplementary material

Supplementary material to this article can be found online at https://doi.org/10.1016/j.catena.2025.109118.

## Data availability

Supplementary Material 1: “Compliance details between the tree-ring chronology, the storm archives and the reanalysis data” of the paper Pouzet et al. in CATENA “Multi-proxy documentation of past storm (Original data) (Mendeley Data)

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