# Different temperature responses of mountain rockwalls, soils, and lakes to summer heat waves

Rosalie Bruel, Florent Arthaud, Florence Magnin, Raphaëlle Napoleoni, Colin van Reeth, Vincent Augé, Clotilde Sagot, Mathieu Fructus, Carole Birck, Philippe Choler

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## To cite this version:

Rosalie Bruel, Florent Arthaud, Florence Magnin, Raphaëlle Napoleoni, Colin van Reeth, et al. Different temperature responses of mountain rockwalls, soils, and lakes to summer heat waves. Regional Environmental Change, 2026, 26 (1), pp.34. 10.1007/s10113-025-02517-3. hal-05490874

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# Different temperature responses of mountain rockwalls, soils, and lakes to summer heat waves

Rosalie Bruel¹,²,³ ID · Florent Arthaud¹,²,³ ID · Florence Magnin⁴ ID · Raphaëlle Napoleonis⁵ · Colin Van Reeth⁶ ID · Vincent Augé⁷ · Clotilde Sagot⁸ · Mathieu Fructus⁹ ID · Carole Birck⁵ · Philippe Choler¹⁰,¹¹ ID

Received: 11 July 2025 / Accepted: 21 December 2025

© The Author(s) 2026, modified publication 2026

## Abstract

Mountain ecosystems are highly sensitive to climate variations. At fine spatial scales, high-elevation microclimates play a critical role in shaping biodiversity, hydrological processes, and ecosystem services, while also influencing the occurrence of natural hazards such as landslides, avalanches, and floods. Heat waves, which have been increasing in frequency and intensity due to global climate change, present significant challenges to these vulnerable environments. This study examines the impacts of summer heat waves on mountain microthermal conditions in three compartments: rockwalls, soils, and lakes. We assembled data across a latitudinal and elevational gradient in the French Alps including years of two recent heat wave events (2015 and 2022). We calculated thermal indicators to evaluate the buffering or amplifying effects of the atmospheric signal on the investigated compartments. The average summer temperature and the growing/thawing degree days were more responsive to heat waves than the phenological indicator (e.g., spring mixing date in lakes) and maximum temperature. We found significant anomalies for both the 2015 and 2022 heat waves across almost all compartments and indicators. Lakes tended to amplify atmospheric temperatures (especially in 2022) whereas rockwalls and soils tended to buffer them. However, residues from the relationship between compartment and atmospheric temperatures were large during heat waves, suggesting that these events may reduce the compartments' buffering capacity. Our study underscores the importance of long-term monitoring of microthermal conditions to provide a more integrative assessment of mountain ecosystem response to extreme meteorological events.

**Keywords** Heat wave · Mountain · Microthermal conditions · Near-surface temperature · Buffering/amplifying effect

## Introduction

Due to anthropogenic climate change, the frequency and intensity of heat waves are increasing and are projected to rise further, with exacerbated stress on ecosystems and human societies (IPCC 2023a). The year 2022 was one of the warmest ever recorded in Europe. Western Europe was particularly impacted with a mean annual air surface temperature of more than 1.5 °C above the 1991–2020 average (World Meteorological Organization 2023). Severe warm spells hit the region during the spring and summer. In mountains, these spells caused an unprecedented glacier mass loss (Cremona et al. 2023), enhanced permafrost degradation (Kellerer-Pirklbauer and Eulenstein 2023; Magnin et al. 2024), caused massive wildfires (San-Miguel-Ayanz

et al. 2023), and severe drought with a resulting reduction of primary productivity (Choler 2023). This 2022 episode is part of a decadal trend towards warmer and drier summers (Büntgen et al. 2021; Lu et al. 2023). Europe and more specifically European mountains are heavily impacted by these changes (Kotlarski et al. 2023), and echoing the global trend, it is very likely that the frequency and intensity of heat waves will increase in the near future (Coumou and Rahmstorf 2012; Russo et al. 2014; Seneviratne et al. 2012). Mountains are particularly vulnerable to the rise of air temperature because of temperature effects on natural hazards, on snow cover regime and water resources, and on the dynamics of species and communities (Kullman 2010). The cascading effects on socio-economic activities mainly affect agriculture, tourism, and energy supply (IPCC 2023b).

The local temperature at the land-air interface substantially differs from the air surface temperature measured by weather stations (Marta et al. 2023; Zellweger et al. 2019). Understanding the sign and magnitude of this temperature

Communicated by Wolfgang Cramer

Extended author information available on the last page of the article
difference, also called “offset” (Bramer et al. 2018), is pivotal, because organisms experience small-scale conditions that are only indirectly related to macroclimate. Besides, the conditions also vary within a landscape due to local features. For example, within a 1.25-ha high-altitude pasture, mean seasonal temperature and humidity showed variations as large as those observed between two sites separated by nearly 500 m in elevation (Ohler et al. 2020). Such microclimatic variability can profoundly influence vegetation cover and species composition, with potential consequences for ecosystem services (Ohler et al. 2020). In recent years, there has been a renewed interest in measuring and modeling local thermal conditions, with a strong focus on forest understory and soil temperature (Lembrechts et al. 2022). Microclimate conditions influence plant species distribution (Choler 2018; Lembrechts et al. 2019; Zellweger et al. 2020) and productivity of terrestrial and freshwater ecosystems (Chen et al. 2024). In high mountain areas, local microclimate variations can modulate the amplitude of permafrost degradation, enhancing rockfall activity (Legay et al. 2021; Luethi et al. 2015). However, most of these studies have considered average local conditions and not specific extreme events, although we know that extreme events may have disproportionate roles in driving the dynamics of natural systems (e.g., Cheung and Frölicher 2020). The extent to which microclimates are impacted by extreme meteorological events remains largely uncertain, and this impedes our ability to anticipate how key processes of life-supporting systems will respond to the projected trends of global warming.

Several reasons explain our poor knowledge of the local thermal conditions in mountains facing heat waves. First, monitoring at high elevation is notoriously difficult and there are few instrumented networks able to simultaneously document the microclimate in different ecosystems of mountain catchments. Second, mountains host a large variety of ecosystems in relation to gradients of topography, soil depth, snow, ice cover, and vegetation cover, as well as presence of glaciers, which can locally cool the air which then flows down into valleys, leading to a fine-scale spatial variation of the mechanisms responsible for the offset between macro and microclimate (Salerno et al., 2023; Shaw et al. 2025). This offset is largely overlooked in climate models and monitoring initiatives (Körner 1998; Shaw et al. 2025). Last, the seasonality of the temperate mountain climate with a prolonged snow cover period has a predominant influence on local thermal regimes (Johnston et al., 2024). In a context of rising temperature, the snow cover dynamics may even lead to counterintuitive effects on local temperatures. For example, the risk of local soil freezing may be amplified during a warm spring if the vanishing snowpack does not sufficiently insulate the ground (Groffman et al. 2001). The actual thermal response of mountain ecosystems to heat waves may therefore be different from that calculated

from air temperature. Yet, accurate quantification of thermal response is essential to evaluate the impacts of extreme events on multiple ecosystem services, including, for example, plant phenology, recreational activities (Willwerth et al. 2023), and preservation of biodiversity (Zeng et al. 2025).

While most studies have focused either on the atmospheric signal or on individual ecosystem compartments (e.g., soils, Zeng et al. 2025), none to our knowledge has simultaneously compared their thermal responses to common heat wave events. Such a cross-compartment approach may provide new insights into the differential sensitivity of mountain systems to climate extremes. Soils, rockwalls, and lakes are key interfaces governing energy and matter exchanges with the atmosphere, with cascading effects on critical ecosystem services such as gravitational hazard regulation, pastoral productivity, and freshwater resources (Walter et al. 2020; Peringer et al. 2022; Pastorino et al. 2024; Islam et al. 2025). Examining these compartments together is therefore crucial to capture the diversity of microclimatic responses and better understand their implications for ecosystems and human activities.

The aim of this paper is to evaluate how within-ecosystem, local temperatures have been impacted by the recent heat waves. Our approach is unique in that it provides a common analytical framework for multiple compartments, i.e., high-elevation rockwalls, mountain soils, and mountain lakes. We benefit from a dense network of instrumented sites in above-treeline areas of the French Alps (Bonet et al. 2015). We also build on regionalized climate re-analyses (Vernay et al. 2022) enabling us to evaluate the offset between macro- and microclimate at the mountain massif scale and in different topographical conditions. We specifically address the following questions: (i) Can we identify relevant thermal indicators to investigate how different compartments respond to heat waves? (ii) How did the 2015 and 2022 heat waves (macrothermal conditions) affect local (microthermal conditions) temperatures in these compartments? (iii) Do different compartments respond differently to heat waves? (iv) At each site, is there any relationship between atmospheric temperature and the temperature in each compartment, and if so, do different compartments buffer or amplify the atmospheric signal during heat wave years?

# Material and methods

## Study sites

We analyzed temperature data at 95 measurement points spread in the entire French Alps (Fig. 1A). Twenty-six of these points are in rockwalls from 2462 to 3714 m a.s.l., 44 in soils from 483 to 2674 m a.s.l., and 25 in lakes from
<figure><img src="image_3.png" /><figcaption>**Fig. 1** (A) Location of the study sites in the French Alps. Elevation was downloaded from https://mapscaping.com/global-elevation-data-download-tool/. The black thick lines outline the different massifs. (B) Cumulative availability of stations (stacked bars, primary axis) and average number of years with data (secondary axis, open diamonds), with standard deviation. (C) Mountain view with representation of the location of the three compartments monitored for temperature: rockwalls (purple), soils (yellow), and lakes (blue). Point locations in the 3D view are illustrative only and do not correspond to real study sites. Photos are respectively for Aiguille du Midi (ADM</figcaption></figure>

in this study, Chamonix, France, © Wikipedia), Roche Noire watershed next to Lautaret pass (RON sites in this study, Monêtier-les-Bains, France © P. Choler) and Lac du Grand Laus (MAL05 in this study, Aiguilles, France, © R. Bruel). (D) Cross-section of the three compartments illustrating the depths at which temperature is measured, respectively in a borehole (rockwall), a hole (soil), and along a vertical weighted chain with a buoy on the top to help with flotation (lake). Red closed triangles indicate the location of temperature sensors used in this study, and orange open triangles indicate location of additional sensors at the station that were not used in this study

1983 to 2820 m a.s.l. (Supplementary Materials, Fig. S1). The measurement sites are maintained through different projects and observatories, with the common objective to monitor the impact of climate change on mountain ecosystems.

Rockwalls data (from a permafrost observatory) are found at higher elevations, mountain lakes lie above the treeline (≈1800–2000 m a.s.l.), and soils extend across a wide elevational range, including grazed areas (Bürli et al. 2021). All
slope aspect orientations are covered. Those time series have been selected based on their quality (limited gaps in data), comparability, and spatial and temporal coverage. Nonethe- less, most of the available time series are recent and a large part of them start after 2013 (lakes) or by 2020 (rockwalls) (Fig. 1B).

Rockwall temperature is monitored with Geoprecision® loggers (acc. ±0.1 °C) placed at a depth of c. 10 cm to get information on possible permafrost distribution and the surface energy balance. They register the near-surface temperature at 2-h intervals, but were installed recently (between 2019 and 2021). Additionally, 6 sites have 10- to 20-m depth boreholes equipped with thermistor chains to monitor permafrost evolution. The thermistor chains host 15 sensors relatively close to each other near the surface (30 cm depth) and spread further apart deeper. They record temperature at 3-h intervals. Three of them record the temperature for more than 10 years. As most sensors were installed recently, we selected only 5 out of the 26 sites included in the network for the core analysis of this study. The shorter time series were used in the robustness analysis (see details in the “Data analyses” section). Technical information on the borehole installations is reported by Magnin et al. (2024). Bedrock permafrost evolution is monitored in the framework of the PermaFrance network; data are available through the GTN-P portal (https://gtnp.arcticportal.org/).

Sites monitored for soil temperature are equipped with miniaturized and standalone data loggers (Hobo pendant UA, Onset Computer Corporation, Bourne, MA, or GeoPrecision M-Log5, GmBH; all with acc. ±0.5 °C) or Phenoclim stations (acc. ±0.5 °C) (Laigle et al. 2022). These loggers are located between 5 and 10 cm below the ground surface, i.e., in the topsoil layer occupied by the dense root system of perennial plants (mostly grasses). Measurements are done on an hourly basis, although a few have a 2-h logging interval pre-2014. Phenoclim stations record data at 15-min intervals; for consistency, only the first record of each hour was retained. This monitoring of soil temperature is part of several ongoing long-term projects examining soil-climate-vegetation interactions in above-treeline ecosystems of the French Alps (Choler 2018; Choler et al. 2022; Laigle et al. 2022). For this study, we selected all sites for which continuous time series starting in early 2015 were available, i.e., 44 sites. Data are in open access and hosted under a Creative Commons license at https://entrepot.recherche.data.gouv.fr/dataverse/ZAA SOIL_TEMP and https://frost-viewer.monitoring.creamontblanc.org/.

The 25 lakes in this study are equipped at their deepest point with a vertical cable anchored with a weight and floating with the help of a buoy. Temperature loggers (Tinytag Aquatic2 (acc. ±0.5 °C), PME Minidot (acc. ±0.1 °C), and HOBO Water Temp Pro U22-001 and MX2203 (acc. ±0.2 °C) recording hourly temperatures are located

along the cable at various locations and different depth resolutions. For this study, only temperature data from the subsurface (–1.5 to –2 m depending on the lake) was used, although we resorted to the data from the deepest temperature sensor (2 m above the lake bed) to visually confirm the Spring mixing date (see indicator Recovery Day, section *Indicator selection*). Data acquisition started as early as 2006, and all lakes were equipped by 2015, as part of the networks *Lacs Sentinelles* (http://w3.avignon.inra.fr/geonetwork_anaee/srv/fre/catalog.search#/metadata/b8249490-1ef8-458a-9bb1-ddd320749dc4) and *Réseau National de suivi de la Température des plans d'eau* (RNT-PE, http://geocatalogue.ecla.inrae.fr/geonetwork/srv/fre/catalog.search#/metadata/15881d2b-2310-4ea2-b80e-cbf8a48e1869) (Table S1). Data are stored in the database bd-thermie hosted by Pôle ECLA (OFB-INRAE-USMB, Aix-en-Provence) and can be visualized through the dashboard “DATA-ECLA”: https://dataecla.fr/ (https://dataecla.fr/dashboards/thermie/).

## Meteorological variables

We used the S2M (SAFRAN-SURFEX/ISBA-Crocus-ME Pra) re-analyses that simulate surface conditions and seasonal snow cover as a function of elevation in 300 m increments for 23 massifs in the French Alps to obtain air temperature and snow cover (Vernay et al. 2022). The S2M model combines observed weather station data, numerically predicted air temperature, and snow cover based on topographical features (elevation, slope, aspect).

First, to characterize the average anomaly across the Alps, we calculated for each year the average temperature between 1 July and 31 August for the 1950–2250 m a.s.l. elevation range as an integrative measure of snow-free season temperature for each year. We then transformed the values into anomalies by subtracting the average calculated for the 1991–2020 reference period. We also extracted the snow melt-out date, i.e., the first day of no snow cover, and transformed each yearly value into an anomaly using the same reference period (Fig. 2A). We also visualized daily temperature anomalies for the two main heat wave years relative to the 1991–2020 reference period. For each year, we calculated daily differences between the year of interest and the reference mean and aggregated these to monthly averages (visualized above the panels, Fig. 2B). To assess the distribution of temperature deviations, we counted the number of days within 2.5 °C intervals, highlighting temperature ranges that were less or more frequent than in the reference period (visualized on the right side of the panels, Fig. 2B).

Second, we ran the S2M re-analysis on the 95 plots where local temperature measurements were available. Compared to the standard products assuming open field situations and the median elevation of each 300 m band, this simulation
<figure><img src="image_4.png" /><figcaption>**Fig. 2** A Scatterplot of snow melt-out date anomaly and summer (July–August) temperature anomaly. Both variables were computed yearly for 1959–2022 using 1991–2020 as the reference period. B Daily temperature in 2015 (top) and 2022 (bottom) compared to the reference of 1991–2020 (thicker black line). Monthly average anomalies are shown for both years above each panel. Shades of blue indicate a negative anomaly, i.e., average monthly temperatures were lower than the average calculated for the 1991–2020 reference period,</figcaption></figure>

accounts for local conditions in terms of elevation, slope, and aspect of the plots and the solar radiation masks, and results in more appropriate estimates of the snow cover and temperature-related indicators. Surface conditions are simulated every 15 min from 1970 onwards. Here, we relied on daily statistics (mean, maximum, and minimum) of temperature to calculate the indicators.

## Thermal indicators

For all three compartments and the atmosphere, we calculated the same four thermal indicators per site and per year: (1) average summer temperature ($T_{summer}$); (2) thawing or growing degree days (TDD, GDD); (3) maximum temperature ($T_{max}$); (4) the day when temperatures exceeded 0 °C (air, rockwall, soil) or 4 °C (lakes), which we named recovery day ($d_{recovery}$) (Table 1, Fig. 3).

Average summer temperature is an indicator easily comparable across sites, years, and compartments. $T_{summer}$ was calculated as an average of daily temperature from July 1 to August 31, a period during which most catchments at our study sites are no longer snow-covered and thus reflects actual summer conditions (Perga et al. 2023; Choler et al. 2025a, b). Including June, as is conventionally done in

and shades of red indicate a positive anomaly. The number of days with temperature anomalies per 2.5 °C increment is shown on the right of each panel; yellow and purple respectively indicate less and more days in the given temperature range compared to the reference period 1991–2020. Meteorological data are derived from the S2M re-analysis for the elevational band 1950–2250 m, averaged across the 23 massifs

lowland studies (e.g., Materia et al. 2022), would not be appropriate in these alpine environments because many areas still retain snow at that time.

We calculated degree days to reflect more accurately each system period of increased biological activity. Degree days (GDD and TDD) were calculated between day 1 and n according to Eq. 1:

$$DD_n = \sum_{i=1}^{n} \max(T_{daily_i} - T_{base}, 0) \quad (1)$$

where $T_{daily_i}$ is the average daily temperature, $T_{base}$ is the base temperature at which development is thought to occur. We used $T_{base}=0$ °C (above freezing) except for lakes, where we used $T_{base}=4$ °C. Liquid water measured at 2 m depth does not go below 0 °C, which justified using a different $T_{base}$ for lakes. Applying 0 °C as $T_{base}$ in lakes would lead to GDD accumulation starting from January 1, whereas for soils and rockwalls accumulation begins in spring, when temperatures exceed 0 °C. Instead, we used 4 °C because at this temperature water density is highest, implying that, if the surface temperature rises above 4 °C, the lake loses its inverse stratification and vertical mixing resumes, triggering spring hydrodynamics. Besides, in lakes, 4 °C generally represents the minimum temperature for the growth of
<table><caption>**Table 1** Data used for each indicator. Differences in threshold or computation are highlighted in bold</caption><thead><tr><th colspan="2">INDICATORS</th><th colspan="4">COMPARTMENTS</th></tr><tr><th>Name</th><th>Abbreviation</th><th>Air</th><th>Rockwall</th><th>Soil</th><th>Lake</th></tr></thead><tbody><tr><td>Average summer (JA) temperature</td><td>T<sub>summer</sub></td><td>Daily</td><td>Daily</td><td>Daily</td><td>Daily</td></tr><tr><td>GDD/TDD</td><td>GDD/TDD</td><td>Daily, T<sub>base</sub> = 0 °C<br>January 1st–August 31st</td><td>Daily, T<sub>base</sub> = 0 °C<br>January 1st–December 31st</td><td>Daily, T<sub>base</sub> = 0 °C<br>January 1st–August 31st<br>1- to 2-h interval</td><td>Daily, T<sub>base</sub> = 4 °C<br>January 1st–August 31st<br>1- to 2-h interval</td></tr><tr><td>Maximum temperature</td><td>T<sub>max</sub></td><td>T<sub>max</sub> directly from simulation</td><td>2- to 3-h interval</td><td>1- to 2-h interval</td><td>1- to 2-h interval</td></tr><tr><td>Annual recovery date</td><td>d<sub>recovery</sub></td><td>Daily, day at which<br>T<sub>mean</sub> > 0 °C (for<br>at least 12 out of<br>15 days)</td><td>Daily, day at which<br>T<sub>mean</sub> > 0 °C (for at least<br>12 out of 15 days)</td><td>Daily, day at which<br>T<sub>mean</sub> > 0 °C (for<br>at least 12 out of<br>15 days)</td><td>Daily, day at which<br>T<sub>mean</sub> > 4 °C (no dura-<br>tion limit as lakes in<br>the study directly enter<br>stable stratification)</td></tr></tbody></table>

organisms (Christianson and Johnson 2020). The period of computation was January 1st to August 31st except for rockwalls where *n* was the last day above *T<sub>base</sub>*, because *TDD* is generally calculated for the whole thawing season in permafrost ground (Harris 1982). *GDD* and *TDD* are therefore the sum of temperature above *T<sub>base</sub>* (0 °C for rockwall and soil, 4 °C for lake) from January 1st until July 31st (soils and lakes) or the last day of the year above *T<sub>base</sub>* (rockwalls). For near-surface soil temperature, values above 0 °C are associated with diurnal temperature fluctuations and typically indicate snow-free conditions. Vegetation growth rapidly follows the snow melt-out day at the study sites (Choler 2018).

Maximum temperature (*T<sub>max</sub>* ) was the only indicator calculated from the hourly data with logging at 1-h intervals (most soil and lake, a few had 2-h logging intervals before 2014) and 2- to 3-h intervals (rockwall).

The annual day of recovery (*d<sub>recovery</sub>* ) is the first day of year (DOY) when temperature was above *T<sub>base</sub>* (0 °C for bedrock and soil, 4 °C for lake) for at least 12 out of 15 days (Eq. 2):

$$d_{recovery} = \min\{j \in W | \sum_{k=0}^{14} 1(T_{j+k} > T_{base}) \ge 12\} \quad (2)$$

where 1(.) is the indicator function (1 if true, 0 otherwise), and *j* ∈ W. W is the set of candidates for first date {d<sub>min</sub>, d<sub>min</sub> + 1, ..., 182}, starting at d<sub>min</sub>, i.e., the day the minimum temperature was reached for each given winter, defined as any date between January 1st and July 1st, following Eq. 3:

$$d_{min} = \operatorname*{argmin}_{i \in [1,182]} T_i \quad (3)$$

For instance, *d<sub>min</sub>* was in early March 2022 for the soil data illustrated in Fig. 3B.

T<sub>summer</sub> and GDD/TDD were therefore chosen to evaluate how warm the summers were on average, *T<sub>max</sub>* was indicative of the extremes, and (*d<sub>recovery</sub>* ) was a time-dependent indicator, to document the moment at which increased ecological activity may have taken place on the watershed/in the compartment.

We discarded years for T<sub>summer</sub> when less than 95% of the data were available (maximum of 3 days missing), and we did not consider the maximum value to be correct if it was the first or the last value of the series (for example, if the maximum value was measured on August 20th but the sensors were removed from August 21st or installed on August 19th). When computing daily averages, we discarded days with 50% or less data available (at least 12 h of data available for a sensor measuring at an hourly interval). For degrees day calculation, we interpolated between missing values but discarded the resulting value if less than 95% of the data were available overall.

We assessed the sensitivity of thermal indicators to temporal resolution by subsampling the original 1-h time series to 2-, 3-, 4-, and 6-h intervals (the recording intervals is 1 h in most soil and lake dataset but is 2–3-h intervals in rock dataset, and 2–3-h intervals in some soil and lake dataset). Across all compartments, subsampling had a negligible effect on computed metrics: mean differences were <0.003 °C for T<sub>summer</sub>, <0.6 °C day<sup>-1</sup> for GDD/TDD, and <0.1 °C for T<sub>max</sub>. d<sub>recovery</sub> differed on average by <1 day between 1- and 2-h intervals. These small differences relative to interannual variability and sensor precision confirm the robustness of the indicators to moderate changes in sampling frequency (Supplementary Materials 2).
**Fig. 3** Examples of micro-thermal conditions time series and thermal indicators for the three studied compartments A rockwall (ADMNO in this study), B soil (RON_KS_046 in this study), and C lake (ANT74 in this study). In each panel, we show the average temperature (thick black line) and its confidence intervals (thinner black lines) for the years available. The colored lines show the temperature for the period of interest between October 2021 and December 2022 per compartment, spanning one complete hydrological year (from October to September) and the first few months of the next, which together represent 15 months of continuous data. In panel C, the lake's deepest temperature is also shown in light blue to help visualize the stratification and mixing periods. Indicators used are illustrated on each panel: [1] corresponds to the maximum temperature recorded during the hydrological year, [2] is the average temperature for July–August, [3] corresponds to the period for which TDD/GDD was calculated (TDD/GDD is illustrated with the green area below each graph), and [4] shows the day of the year at which temperature goes above 0 °C (rockwall, soil) or 4 °C (lake). Note the different y-scale (temperature amplitude) per compartment

<figure><img src="image_5.png" /><figcaption>(A)</figcaption></figure>

Rockwall (Aiguille du Midi, North-West borehole),
reference period: 2009-2021

<figure><img src="image_6.png" /><figcaption>(B)</figcaption></figure>

Soil monitoring in Roche Noire watershed,
reference period: 1999-2021

<figure><img src="image_7.png" /><figcaption>(C)</figcaption></figure>

Lake Anterne, reference period: 2006-2021

Day

<figure><img src="image_8.png" /></figure>

## Data analyses

Anomalies calculated for the years 2015 and 2022 across the four compartments (atmosphere, rockwall, soil, and

lake) were analyzed in three different ways: (i) comparison of heat wave and reference years anomalies distributions to test whether anomalies observed those years were outside the observed variability, (ii) comparison of the responses
across the two heat waves within compartment, to assess whether 2015 and 2022 led to similar responses, and (iii) per heat wave year, comparison of the different compartment responses to test whether different compartments have different amplitude of responses. The work was systematically done for the four key indicators ($T_{summer}$, GDD and TDD, $T_{max}$, $d_{recovery}$).

## Anomalies

For each indicator, we computed the difference in value between the heat wave years and the rest of the available data (Eq. 4):

$$ \Delta_n = x_n - \forall x \in S \setminus \{n\} \quad (4) $$

where $x$ is the value of the indicator, $n$ is the year of the anomaly, and $S$ is all the values of the indicator calculated for a given site for all the years available. Analyses were only conducted when the indicator was available for at least 4 years. We did not exclude 2015 from the calculation when calculating the $\Delta_{2022}$ nor 2022 when calculating $\Delta_{2015}$. We found no impact of the number of years available beyond our threshold of 4 years on the anomaly that was calculated (Supplementary Materials 3).

Anomalies per compartment and per heat wave year were then tested for significant deviation from the distribution of non-heat wave (“reference”) years (comparing this time $\Delta_{2015}$ to $\Delta_{2022}$). To limit bias, and although we had atmospheric temperature simulated for 31 years, we only kept the atmospheric data when compartment data were available as well for that year. Within each compartment, anomaly values were compared between the two heat wave years (2015 vs. 2022) using paired t-tests when the differences were approximately normally distributed (Shapiro-Wilk test, $p > 0.05$) and with Wilcoxon signed-rank tests otherwise. We used the functions `t.test()` and `wilcox.test()` from the `stats` package for each case (R Core Team 2023). Finally, within each heat wave year, we compared responses across compartments to determine whether certain compartments reacted more or less strongly than others. We used the parametric `t_test()` function in the `rstatix` package (Kassambara 2023) to conduct pairwise comparisons and used the Bonferroni correction to adjust the $p$-value when there were more than two levels to compare.

## Response to driver

Sites with at least 5 years of data were selected to analyze the relationships between indicators calculated for the microclimate and the air temperature ($I_{compartment} \sim I_{atmosphere}$, where $I$ is a given indicator). These were tested using three models: (1) a linear model on raw values, (2) a linear model

on log10-transformed values, and (3) a second-order polynomial model to assess potential bell-shaped or amplified relationships. Models were run per site. The best-fitting model was chosen based on the Akaike Information Criterion (AIC). For the selected model (lowest AIC), the following metrics were extracted: significance level (n.s., $0.01 \le p < 0.05$, $p < 0.01$) and slope. Additionally, for each compartment (rockwall, soil, lake), we summarized the number of relationships per significance level and examined the slopes for models with $p < 0.05$.

For the significant models, we checked whether the residuals of the responses (compared to prediction) were positive or negative and whether the residuals differed for the heat wave years compared to the other years. To do so, we used a t-test when the distribution of residuals was normal (controlled using a Shapiro test), and a Wilcoxon test when it was not.

All data handling, analyses, and figures drawing were conducted in R (R Core Team 2023), using the R packages `lubridate` (Grolemund and Wickham 2011), `rstatix` (Kassambara 2023), the `tidyverse` (Wickham et al. 2019), `sf` (Pebesma and Bivand 2023), `raster` (Hijmans 2023), and `patchwork` (Pedersen 2024).

## Results

### Meteorological conditions

At the scale of the 23 massifs in the French Alps, and for the 1950–2250 m elevational range, the years 2015 and 2022 showed the two record anomalies for the $T_{summer}$ over the 1959–2022 period, with respectively +2.4 and +2.3 °C compared to the reference period 1991–2020. In parallel, these years were characterized by early snow melt-out dates (23 and 21 days earlier than the reference period), ranking 2015 and 2022 as the third and fourth years with earliest snow melt-out dates over 1959–2022 (Fig. 2A). Although the average anomaly in $T_{summer}$ was higher in 2022 ($p < 0.0001$, Table 2B), higher $T_{max}$ were reached in 2015 compared to 2022 ($p < 0.0001$), and 2022 was characterized by consistently higher temperature throughout the year (Fig. 2B). Compared to the 1991–2020 reference, 2015 and 2022 were characterized by fewer days with temperature below 0 °C (at elevation between 1950–2250). 2015 was characterized with more extreme values compared to 2022 (Fig. 2B, Table 2B).

### Impacts of the 2015 and 2022 heat waves on microthermal conditions

The 2015 and 2022 heat waves led to significant microthermal anomalies in the three compartments considered in this study (Table 2, Fig. 4). Twenty out of twenty-four
Differences in indicator values between
the heat waves years and the rest of the available data

<figure><img src="image_9.png" /><figcaption>**Fig. 4** Differences between indicators calculated in 2015 (light yellow) and 2022 (orange) compared to the rest of the data available, per site. Violin plots (dashed border, lighter filling) show the normalized</figcaption></figure>

distribution for each indicator of the values. Boxplots show the distri-
bution of anomalies calculated across sites, per compartment
<caption>**Table 2.** (A) Anomalies values for 2015 and 2022 compared to the rest of the dataset, per compartment (atmosphere for reference) and indicator, with standard deviation in parentheses ("Anomaly" columns), as well as several information on the test: number of observations for the anomaly year/number of observations for the reference years, test type (paired parametric Paired *t*-test (P) if normality of difference, or non-parametric Wilcoxon signed-rank (NP) otherwise), *t*-test/Wilcoxon statistic (mean or median difference for parametric and non-parametric test, respectively), and significance level. In the anomaly column, values from the heat wave year with the largest</caption>

anomaly are shown in bold. A positive statistic indicates that the heat wave year has a higher mean or median than the reference period (see columns "average anomaly"). (B) Comparison of anomalies between the two heat waves years (2022 vs. 2015) per compartment, with paired-test statistics, parametric assumption (P=parametric paired *t*-test, NP=non-parametric Wilcoxon signed-rank test), mean or median difference, and statistical levels. When *p* ≥ 0.05 (n.s.), the rounded *p*-value is included; other levels are: *p* < 0.05 (*), *p* < 0.01 (**), *p* < 0.001 (***), *p* < 0.0001 (****)

<table><thead><tr><th rowspan="3"></th><th rowspan="3">Compartment<br>(Atmosphere<br>for reference)</th><th colspan="4">(A) Comparing heat waves years with reference years</th><th colspan="2">(B) Heat waves comparisons</th></tr><tr><th colspan="2">Average anomaly per compartment and<br>per year (± standard deviation)</th><th colspan="2">Number of sites for the anomaly year /<br>number of sites for the reference years;<br>parametric assumption; statistic;<br>significance level</th><th rowspan="2">test statistic (parametric assumption<br>and mean/median difference) and<br>significance</th><th rowspan="2">2022 vs. 2015</th></tr><tr><th>2015</th><th>2022</th><th>2015</th><th>2022</th></tr></thead><tbody><tr><td rowspan="4">Temp. JA</td><td>Atmosphere</td><td>1.45 (+/-0.41)</td><td>1.62 (+/-0.59)</td><td>58/70; P; 26.91; ****</td><td>57/67; P; 20.89; ****</td><td>1016.5 (NP, median = 0.23) ****</td><td></td></tr><tr><td>Rockwall</td><td>0.64 (+/-0.46)</td><td>1.56 (+/-0.24)</td><td>3/3; P; 2.44; 0.135</td><td>3/3; P; 11.3; **</td><td>2.66 (P, mean = 0.92)</td><td>0.12</td></tr><tr><td>Soil</td><td>1.24 (+/-0.87)</td><td>2.14 (+/-1.43)</td><td>39/43; P; 8.9; ****</td><td>34/42; NP; 594; ****</td><td>423 (NP, median = 0.54)</td><td>**</td></tr><tr><td>Lake</td><td>1.66 (+/-1.97)</td><td>4.1 (+/-1.69)</td><td>16/24; P; 3.38; **</td><td>20/22; P; 10.25; ****</td><td>105 (NP, median = 1.72)</td><td>**</td></tr><tr><td rowspan="4">GDD/TDD</td><td>Atmosphere</td><td>117.97 (+/-57.65)</td><td>219.06 (+/-102.86)</td><td>46/71; P; 20.08; ****</td><td>50/66; NP; 1275; ****</td><td>774 (NP, median = 164.95) ****</td><td></td></tr><tr><td>Rockwall</td><td>95.92 (+/-47.61)</td><td>336.48 (+/-151.09)</td><td>3/9; P; 3.49; 0.073</td><td>4/4; P; 4.47; *</td><td>1.61 (P, mean = 135.51)</td><td>0.25</td></tr><tr><td>Soil</td><td>147.21 (+/-82.48)</td><td>296.07 (+/-154.38)</td><td>35/41; NP; 619; ****</td><td>33/42; P; 10.81; ****</td><td>4.51 (P, mean = 126.94)</td><td>****</td></tr><tr><td>Lake</td><td>125.91 (+/-121.67)</td><td>364.71 (+/-88.77)</td><td>8/21; P; 5.72; ***</td><td>13/20; P; 20.83; ****</td><td>3.25 (P, mean = 180.96)</td><td>*</td></tr><tr><td rowspan="4">Tmax</td><td>Atmosphere</td><td>0.82 (+/-0.96)</td><td>-0.99 (+/-1)</td><td>56/80; NP; 1473; ****</td><td>57/70; NP; 194; ****</td><td>65 (NP, median = -1.29)</td><td>****</td></tr><tr><td>Rockwall</td><td>1.49 (+/-0.29)</td><td>0.6 (+/-1.57)</td><td>3/13; P; 8.88; *</td><td>6/6; NP; 16; 0.295</td><td>3 (NP, median = 0.43)</td><td>1</td></tr><tr><td>Soil</td><td>2.61 (+/-3.24)</td><td>1.6 (+/-2.63)</td><td>37/43; P; 4.91; ****</td><td>32/42; P; 3.35; **</td><td>-2.25 (P, mean = -1.65)</td><td>*</td></tr><tr><td>Lake</td><td>1.07 (+/-2.2)</td><td>3.23 (+/-1.19)</td><td>16/24; NP; 115; *</td><td>20/23; NP; 210; ****</td><td>91 (NP, median = 1.01)</td><td>**</td></tr><tr><td rowspan="4">Recovery day</td><td>Atmosphere</td><td>-4.83 (+/-12.96)</td><td>-12.4 (+/-10.96)</td><td>51/83; NP; 509; 0.15</td><td>62/75; NP; 163; ****</td><td>263 (NP, median = -20.25)</td><td>*</td></tr><tr><td>Rockwall</td><td>-12.36 (+/-19.3)</td><td>-32.45 (+/-26.83)</td><td>3/18; P; -1.11; 0.383</td><td>10/10; NP; 0; **</td><td>-0.5 (P, mean = -5.8)</td><td>0.67</td></tr><tr><td>Soil</td><td>-12.23 (+/-9.37)</td><td>-12.2 (+/-10.27)</td><td>39/44; P; -8.15; ****</td><td>35/44; P; -7.03; ****</td><td>0.45 (P, mean = 1.07)</td><td>0.66</td></tr><tr><td>Lake</td><td>-13.57 (+/-5.59)</td><td>-22.34 (+/-4.78)</td><td>10/21; P; -8.6; ****</td><td>17/21; P; -21.43; ****</td><td>-3.81 (P, mean = -10.39)</td><td>**</td></tr></tbody></table>

comparisons were significantly different. $\overline{T}_{summer}$ anomalies were significantly higher than the reference years across all compartments during both heat wave years, except for rockwall in 2015 (mean estimate = 2.44, *p* = 0.135). GDD/TDD values were higher than the reference period across all compartments both years. $T_{max}$ anomalies were highly positive for all compartments in 2015, except for rockwall in 2022. Finally, $d_{recovery}$ occurred on average significantly (*p* < 0.01) earlier across all compartments (negative anomalies), except for rockwall in 2015 where it occurred earlier, but not significantly (-1.11, *p* = 0.383) (Table 2A).

There was no significant difference between 2015 and 2022 for rockwalls, regardless of the indicator considered (*p* > 0.05 in all cases). For lakes, values were consistently higher in 2022 across all indicators (*p* < 0.01 in all cases). For soil, $\overline{T}_{summer}$ and GDD were significantly higher in 2022 ($\overline{T}_{summer}$ median of -0.54 °C in 2015 compared to 2022, *p* < 0.01 and GDD mean of -126.94 °C in 2015 compared to 2022, *p* < 0.0001) whereas $T_{max}$ was lower (-1.65 °C in 2022 compared to 2015, *p* < 0.05) and $d_{recovery}$ anomalies were not significantly different between the 2 years (*p* > 0.67) (Table 2B).

The magnitude of the anomalies was largely similar across compartments in 2015, based on the selected indicators (Table 3). Soil showed a higher $T_{max}$ anomaly than

atmosphere (3.15, *p* < 0.05), and both the soil (-3.42, *p* < 0.05) and lake (-3.99, *p* < 0.01) compartments showed an earlier $d_{recovery}$ anomaly than the atmosphere. Differences between compartments were more pronounced in 2022, especially for lakes. In 2022, the lake compartment showed a higher $\overline{T}_{summer}$ anomaly than the atmosphere (6.03, *p* < 0.0001) and the two other compartments (rockwall, 5.95, *p* < 0.0001; soil, 4.06, *p* < 0.001), a higher anomaly in GDD than the atmosphere (5.31, *p* < 0.001), a higher $T_{max}$ anomaly than atmosphere (12.19, *p* < 0.0001) and soil (2.91, *p* < 0.05), and an earlier $d_{recovery}$ anomaly than atmosphere (-5.83, *p* < 0.0001) and soil (-4.96, *p* < 0.0001). Soil showed a stronger $T_{max}$ anomaly than the atmosphere (4.84, *p* < 0.001) in 2022 (Table 3).

## Modeling the offset between micro and macrothermal response

In modeling the offset between micro (compartment) and macrothermal (atmosphere), we selected the best model by AIC. The most parsimonious model was systematically the linear model on the log-transformed driver and response variables, except for the relation for $\overline{T}_{summer}$ for one rockwall site where the best model was a linear model on non-transformed data. For $\overline{T}_{summer}$, 100% (*n* = 3), 85%
<table><caption>**Table 3.** Pairwise comparison of environmental response. Only the significant relations are shown. The significance level comparing the compartments' response levels is *p* < 0.5 (*), *p* < 0.01 (**), *p* < 0.001</caption><thead><tr><th>Year</th><th>Compartment 1</th><th>Compartment 2</th><th>Temp. J-A</th><th>GDD/TDD</th><th>Tmax</th><th>Recovery day</th></tr></thead><tbody><tr><td>2015</td><td>Soil</td><td>Atmosphere</td><td></td><td></td><td>*</td><td>*(3.15)</td><td>**(-3.42)</td></tr><tr><td>2015</td><td>Lake</td><td>Atmosphere</td><td></td><td></td><td></td><td></td><td>**( -3.99)</td></tr><tr><td>2022</td><td>Soil</td><td>Atmosphere</td><td></td><td></td><td>***</td><td>(4.84)</td><td></td></tr><tr><td>2022</td><td>Lake</td><td>Atmosphere</td><td></td><td></td><td>**** (6.03)</td><td>*** (5.31)</td><td>**** (12.19)</td></tr><tr><td>2022</td><td>Lake</td><td>Rockwall</td><td></td><td></td><td>**** (5.95)</td><td></td><td>**** (-5.83)</td></tr><tr><td>2022</td><td>Lake</td><td>Soil</td><td></td><td></td><td>*** (4.06)</td><td>*</td><td>**** (-4.96)</td></tr></tbody></table>

(***), and *p* < 0.0001 (****). The statistic of the *t*-test is given in parentheses. The data were organized so that in the table, the mean of Compartment 1 is showing a stronger response than Compartment 2

(n=34), and 52.4% (n=11) of rockwalls, soils, and lakes (respectively) had a significant relationship between the indicator value calculated for the compartment and the indicator value calculated for the atmosphere (Fig. 5A). For GDD/TDD, 50% (n=2), 77.5% (n=31), and 40% (n=8) of rockwalls, soils, and lakes had a significant relationship between the indicator value calculated for the compartment and the indicator value calculated for the atmosphere. For both *T*<sub>summer</sub> and GDD/TDD, estimates of the relationship were on average <1 for rockwalls and soils, i.e., indicative of a buffering effect to increases in air temperature, and >1 for lake, i.e., indicative of an amplifying effect to increases in air temperature (Fig. 5B). For maximum temperatures, 50% (n=2), 13.5% (n=5), and 19% (n=4) of rockwalls, soils, and lakes had a significant relationship between the indicator value calculated for the compartment and the indicator value calculated for the atmosphere. For recovery day, none of the 4 rockwalls, 31% (n=13) of the soils, and 5% (n=1) of the lakes had a significant relationship between the indicator value calculated for the compartment and the indicator value calculated for the atmosphere.

Residuals for the heat wave years were singular (i.e., higher or lower than the residuals for the other years) in most cases, especially for the stronger anomalies revealed in the previous steps (Fig. 5C). Specifically, residuals of the relationship were normally distributed only for the *T*<sub>max</sub> and the relation for the lakes and soils GDD (Supplementary Materials 4). Yet, 2015 residuals in lakes for the *T*<sub>max</sub> relation were significantly higher than the other years (*p* = 0.015). For GDD, we found that 2015 and 2022 had significantly higher residuals in soils, when 2015 had significantly higher residuals only for lakes. Conversely, 2015 showed negative residuals for TDD in rockwalls. We found that 2022 had higher residuals on average across compartments in the relationship for *T*<sub>summer</sub>, further confirming the singularity of that year. Finally, we could only test residuals for *d*<sub>recovery</sub> for soils, and found negative residuals in 2015 (Supplementary Materials 2).

## Discussion

We analyzed high-frequency temperature data from three distinct mountain compartments (rockwalls, soils, and lakes) to assess their responses to the extreme heat events of the summer of 2015 and 2022. In response to our initial questions, (1) we identified four thermal indicators that are relevant individually and applicable across compartments. (2) Our results show that heat accumulation within each compartment, reflecting their respective microthermal conditions, was significantly elevated during heat waves compared to other summers, indicating a strong imprint of macroclimate at the compartment level. (3) Using these indicators, we found that all compartments responded to heat waves in the same direction, indicating a coherent thermal response pattern, but differed in response magnitude. Finally, (4) by modeling the relationship between local (microthermal conditions) and atmospheric (macrothermal conditions) temperatures, we observed offsets across compartments. Specifically, rockwalls and soils tend to buffer increases in temperature, while lakes amplify them.

## On the importance of long-term monitoring of thermal conditions

Both the 2015 and 2022 heat waves exhibited prolonged durations and elevated temperatures across the Alps, surpassing the average temperature over the 1991–2020 period by over 2 °C, which is above the values reported for Western Europe by about 0.5 °C (World Meteorological Organization 2023). While the 2022 heat wave received a lot of attention, probably because it was generalized to Europe (Copernicus Climate Change Service 2023), our study reveals that 2015 exhibited similar anomalies in the French Alps, with early snow melt-out dates and high summer temperatures. Understanding how ecosystems are responding to heat waves requires capturing the conditions they are directly exposed to, i.e., their microclimate. This
<figure><img src="image_10.png" /><figcaption>**Fig. 5** (A) Proportion of statistically significant and non-significant relationships between response compartment and atmosphere. (B) Plot of the estimates per indicator and compartment, for the significant relationships. Estimates [-1, 1] suggest a buffering effect, and estimates < -1 or > 1 suggest an amplifying effect for a negative (< -1) or positive (> 1) relation. The boxplot on the right of the scatter points (red filling) shows the estimates' distribution for significant (*p* < 0.05) relationships. (C) Plot of the residuals per indicator, compartment,</figcaption></figure>

and heat wave year for the significant relationships. The violin plot (dashed border) shows the distribution of the residuals for the reference years; the boxplots show the distribution of the residuals for the heat wave years. The individual residuals (one per site) are overlaid on the graph. Significant differences between the mean of the residuals for a heat wave year compared to the reference years are indicated with significance levels above boxplots, with levels being *p* < 0.5 (*), *p* < 0.01 (**), *p* < 0.001 (***), and *p* < 0.0001 (****)

ambition calls for developing and sustaining in the long term a network of in situ sensors, following a protocol that can allow for comparison among the different compartments of the ecosystems.

To the best of our knowledge, this study provides the first assessment of the impacts of such heat waves on mountain thermal conditions at the regional scale and across compartments. The elevated temperatures had quantifiable impacts on the three mountain compartments we studied, although

there were significant differences in the responses of the studied compartments to the same heat wave year, i.e., different offsets. In 2022, the lake compartment exhibited greater anomalies, especially for summer temperature, GDD, and maximum temperature. We also found that rockwalls and soils in our studies tended to buffer increases in air temperature, whereas lakes showed an amplified response to the atmospheric signal. However, across all compartments, the heat wave years tended to have higher residuals in 2022
for the average summer temperature, suggesting that with
more extreme years, the buffering effect of rockwall and
soil may weaken. The linear model used in this study is
not well suited to detect potential threshold effects, but con-
tinued monitoring over additional years may enable testing
for potential non-linear responses. Finally, we found that
the compartments' responses to both years were different,
highlighting that while a positive response from ecosystems
to heat waves can be expected, the magnitude of the effect
can vary.

While 2015 had overall higher maximum temperatures in July and August, 2022 was warm throughout the spring and summer (and fall, not analyzed here). The compartments' responses were stronger for 2022 while the maximum air temperature did not reach the records of 2015, highlighting the need for a high-frequency monitoring program focusing not only on the summer period, but on the whole growing season (and ideally, throughout the winter), and over multiple years. In the context of a mountain environment that is difficult to access depending on the season, autonomous sensors provide an appropriate solution (Marcé et al. 2016). With autonomous high-frequency sensors, we are able to build upon previous findings and show that not only lakes are warming faster than the air (e.g., O'Reilly et al. 2015; Niedrist et al. 2018), but they accumulate more heat during heat waves. In northern climate lakes, the rapid warming trend can be attributed to a reduced ice cover period, exposing the lakes to air temperature and solar radiation for a longer period, further enhanced by a declining trend in cloud cover (O'Reilly et al. 2015). An aggravating factor which could explain the amplifying effect is the high heat capacity of water—two to four times that of rock (depending on composition) and roughly four times that of air (Waples and Waples 2004)—which may amplify the effects of earlier ice-off on mountain lakes warming.

Translating complex mechanisms into comparable
indicators

We show that some indicators can be computed across
compartments, allowing to generalize the impact of cli-
matic events, such as heat waves, to regions (here, the
French Alps). Despite the relationships found for several
sites between air temperature and compartment tempera-
ture for the integrative indicators (T<sub>summer</sub>, GDD/TDD),
other processes drive temperature in these compartments
and are not accounted for in this study. For example, lake
temperature has been shown to be modulated by param-
eters such as water transparency, inflow water tempera-
ture and quantity, cloud cover, rainfalls, and wind speed
(e.g., Guénand et al. 2020; Perga et al. 2018). Lake Arpont
(Vanoise National Park), a lake in our dataset, illustrates
the complexity of temperature dynamics. Lake Arpont was

connected to its glacier until 2019, when another hot sum-
mer led to the glacier retreating and getting disconnected
from the lake; the lake’s surface temperature increased
by +6 °C almost instantly (Supplementary Materials 5).
With the glacier connected to the lake, the average sum-
mer temperature averaged 4.2 °C (± 0.5 °C); it averaged
10.3 °C (± 2.8 °C) between 2019 and 2022 (the last year
presented in this study). The increased standard error
between the two periods illustrates the sudden responsive-
ness of lake surface temperature to air temperature fluc-
tuations. In contrast, Lake Arpont did not show positive
anomalies despite the 2015 heat wave; the glacier pres-
ence totally cancelled the heat wave signal (Supplementary
Materials 5). In fact, the surface temperature was slightly
lower in 2015 which could be attributed to more glacier
melting during warm years (Sadro et al. 2019; Cremona
et al. 2023). Similarly, in soils, warmer springs are not
always expected to result in warmer soil temperature, as
the early loss of snow cover results in less insulation, more
freezing, thereby increased thermal variations (Choler
2018). Here, we compared 2 years with significantly dif-
ferent atmospheric recovery days, but found that 2015 and
2022 soil recovery days were not significantly different,
which could be explained by the above mechanism.

The ecosystems' responses were reduced to one single
measurement point (the sensor closest to the surface in each
case) to facilitate comparisons across sites and compart-
ments. However, temperature available along boreholes in
rockwalls and the water column in lakes suggest that ver-
tical heat response to extreme events should receive more
attention to better describe the response of ecosystems to
climate change (Pilla et al. 2020; Vincent et al. 2020; Wool-
way et al., al 2025). In permafrost conditions such as high
mountain rockwalls, the active layer thickness is a measure
of how deep the seasonal thawing is (Dobinski 2011). In the
example site of Aiguille du Midi (3842 m asl.), the active
layer started deepening 22 days earlier in the year in 2022
compared to the reference period of 2011–2021 (Magnin
et al. 2024) and reached greater depth in 2022 (for example,
–5 m maximum instead of –4 m at one of the monitor-
ing points for the reference period, unpublished data). In
lakes, thermocline depth could be a comparable indicator.
The thermocline refers to the layer in a stratified lake where
water temperature decreases rapidly with depth, separat-
ing the warm surface layer (epilimnion) from the cold deep
layer (hypolimnion). In lakes with a sufficient resolution
of sensor along the water column to calculate thermocline
depth, we found a strong deepening in 2022 (for example,
thermocline depth was on average 2.4 ± 0.6 m deeper in
July 2022 compared to the 9 other years of data available
in Lac du Grand Laus, unpublished data). Computing these
additional indicators requires a higher number of sensors in
the depth dimension. In lakes that stratify, the coupling we
observe between air and lake surface temperature would not be systematically reproduced for lakes' hypolimnion, as deep water temperature was found to be uncoupled from surface water temperature in a global analysis (Pilla et al. 2020). Yet, warming of surface waters in lakes, without necessarily affecting bottom temperatures, enhances thermal stratification and may lead to a transition from cold monomictic (ice-covered lake in winter, no stratification in summer) to dimictic (lake mixes twice a year, before and after the onset of summer and winter stratification) mixing regimes (Zhang et al. 2025). This shift in thermal dynamics can substantially alter internal biogeochemical cycles and potentially affect lake biodiversity (Mesman et al. 2021).

The limited number of years in our datasets (between 4 and 19 years, with a median of 9 years) constrained our analysis. Anomalies are typically calculated using a 30-year reference period (Kawale et al. 2011), but the available microclimate time series were too short to construct such a baseline. Nevertheless, this limitation is unlikely to weaken our conclusions: we still detected significant anomalies across compartments and indicators, even though the reference period for the 2015 analysis included 2022, and vice versa. The short time series may also have influenced how often we detected significant relationships in the models of I<sub>compartment</sub> ~ I<sub>atmosphere</sub>. Rockwall exhibited the fewest significant relationships, although the direction of its responses was consistent with the other compartments (Supplementary Materials 4, Figures S4.1–4). With longer time series—and additional sites—it is likely that these relationships would also reach significance.

## Climate extremes and the risk of crossing tipping points

Climate change is already significantly impacting mountain ecosystems, with quantifiable impacts on biodiversity and the ways humans interact with and manage mountain systems, i.e., practices (Pastorino et al. 2024). Rockfall activities on both the interannual and decadal timescales are positively correlated to summer air temperatures (Stoffel et al. 2024). Rising temperatures are causing many plant and animal species to migrate to higher elevations or latitudes in search of suitable habitats (Büntgen et al. 2017; Anderson et al. 2020; Choler et al. 2021). For instance, a study analyzing over 230,000 hunting records from the Swiss Alps between 1991 and 2013 found that species like the Alpine ibex, chamois, and red deer have shifted their ranges to higher elevation in response to warmer, snow-free autumn conditions (Büntgen et al. 2017). Based on remote sensing, a widespread, almost non-uniform, increase of vegetation greenness has been reported in above-treeline ecosystems (Anderson et al. 2020; Choler et al. 2021) and there is evidence for a disproportionate impact of rising temperatures

on sites where winter snow historically persisted for longer (Choler et al. 2025a, b). In addition, rising temperatures may disrupt existing pasture zonations for domestic herds, potentially reducing the availability of suitable grazing areas (Peringer et al. 2022). Finally, recent studies have shown that mountain lakes' biochemical processes, species abundance, and assemblage compositions were similarly impacted by temperature (Perga et al. 2023; Dory et al. 2025; Tiberti et al. 2025). Indeed, summer heat accumulation results in reduced oxygen concentration in mountain lakes' hypolimnion (Perga et al. 2023), can increase phytoplankton biovolume in lakes dominated by colonial-green assemblages (i.e., assemblages dominated by filamentous Conjugatophytes and colonial Chlorophytes, Dory et al. 2025), and disadvantage cold stenothermal zooplankton taxa and groups (Tiberti et al. 2025).

Although experimental approaches have begun to shed light on the biological consequences of heat waves, empirical observations of organisms experiencing natural extreme heat events remain comparatively scarce. Experimental work in Arctic tundra soils, for example, revealed that while a heat wave treatment did not alter overall testate amoebae abundance, it did trigger species turnover and ultimately increased taxonomic richness, contrasting with earlier evidence of strong negative effects on the overlying plant community (Beyens et al. 2009; Marchand et al. 2006). Observational evidence from an extreme heat event in East China similarly shows that heat waves can induce substantial but taxon-specific responses: soil macrofauna richness and abundance declined 18 days after the event, yet some groups such as earthworms and Enchytraeidae increased, partly linked to changes in soil water content and vertical redistribution within the soil profile (Gao et al. 2024). Mobility appears to play a role as well, with more mobile species generally better able to adjust to thermal stress (Comte and Grenouillet 2013). Together, these studies highlight that heat wave impacts on organisms are complex, strongly context-dependent, and shaped by species' ecological traits and microhabitat conditions.

The increased occurrence of heat waves also requires studying potential threshold effects, as heat waves are superimposing themselves on the long-term trend of rising temperatures. Zeng et al. (2025) conducted a global synthesis to investigate how biodiversity and ecosystem services of terrestrial mountainous environments respond to local temperature variations and found a tipping point of ~10 °C mean annual temperature above which small temperature changes have amplified impacts (this threshold was not reached by most sites in our study, Supplementary Materials 6). Semanzato et al. (2021) found that above a threshold hourly air temperature of 13–14 °C, Ibex sharply decreased their activity, compensating by foraging at cooler times of the day (mornings and evenings) or by moving further upslope.
In mountain lakes that are already highly constrained for
biodiversity due to extreme conditions, high seasonality, and
reduced options for refuge (Catalan et al. 2006), such heat
waves could act as a pulse disturbance for the collapse of
fragile populations. For example, Lake Allos, one of the
studied systems, hosts one of the last populations of arctic
charr (Salvelinus alpinus L.) at such low latitudes. This pop-
ulation, which was introduced long ago and is non-native,
has been shown to be vulnerable to high temperature (Mari
et al. 2021) and oxygen deficits. Besides, warmer tempera-
tures in these naturally fishless but stocked lakes can favor
minnows over salmonids, as minnows are more tolerant
of higher temperatures. This mechanism adds to the even-
tual negative interactions already in place between the two
groups (e.g., Tiberti et al. 2022).

Anticipating mountain ecosystems services
to biodiversity and humans in a changing climate

The ecological impacts of heat waves should be investigated under the lens of the potential buffering or amplifying effect, i.e., offsets (Bramer et al. 2018). While certain ecosystems may exhibit buffering capacity, thereby maintaining key functions or providing microclimatic refuges at the scale of compartments or catchments (Bates et al. 2025), this study shows that amplification can also occur, potentially accelerating the crossing of ecological thresholds. Among the studied ecosystems here, lakes in particular amplified the climatic signal, aligned with the large role inland waters play in heat uptake at the global scale (Vanderkelen et al. 2020).

The development, calibration, and evaluation of spatially
explicit models that can capture both buffering and amplifying
effects depend on integrated, multi-compartment measurements
conducted during and outside extreme events—
highlighting the critical role of coordinated environmental
monitoring networks. Future work may also integrate compartment-specific variables such as slope aspect (soils) or
shading (lakes, soils), or target sites along comparable altitudinal ranges. For instance, topographic shading can reduce
overall summer temperatures in lakes, offering thermal refugia (Novikmec et al. 2025), and slope aspect influences how
temperatures respond to atmospheric signals (Schaefer et al. 2024). The inclusion of these parameters in future works,
combined with longer time series, could robustly assess why
some sites did not show significant relationships at all. Precise knowledge of compartments’ thermal response can help
predict future plant distribution (Kullman 2010; Anderson et al. 2020; Choler et al. 2021), which is a critical first step
to anticipate and potentially adapt practices like grazing in
order to eventually maintain ecosystem continuity and pasture productivity (Brilli et al. 2023).

Under climate change and with consecutive heat waves,
mountainous areas are also identified as refuge areas for

human activities (Zhang et al. 2022). This perception is
largely based on the fact that, despite strong exposure to cli-
mate warming, absolute temperatures in mountainous zones
often remain within ranges more suitable for recreational
activities (Willwerth et al. 2023). As a result, mountain
regions may experience increased traffic and visitor pres-
sure (Pastorino et al. 2024), which, in areas with limited
infrastructure, may generate multiple new impacts (Baron
et al. 2023; Morales-Armijo et al. 2024). Responding to this
growing demand through the development of new infrastruc-
tures can, however, directly damage natural habitats and
biodiversity (Frossard et al. 2023). Furthermore, the very
geological and surface conditions required for mountain
infrastructures—such as rock slopes and surface deposits—
are themselves increasingly impacted by extreme weather
events, permafrost warming, and thawing, posing risks to
both infrastructures (Duvillard et al. 2021; Jacquemart et al.
2024) and recreationists (Mourey et al. 2019). Thus, local
anthropogenic pressures and climate-driven hazards may
act synergistically, while the impacts on biodiversity of a
growing number of users and potential new infrastructure
remain largely unknown. Given the undeniable vulnerability
of mountain ecosystems to climate change, the vision of
mountain zones as refuges for human activities and tourism
infrastructures must therefore be carefully balanced against
their ecological fragility and increasing exposure to natural
hazards (Pastorino et al. 2024).

Conclusion

Extreme weather events are superimposed on the general
warming trend, raising the likelihood of ecosystems cross-
ing critical thresholds, i.e., tipping points, in terms of bio-
diversity, biogeochemical fluxes, and ecosystem services.
In this study, we provide to the best of our knowledge the
first multi-compartment, high-frequency assessments of
heat wave impacts in mountain ecosystems, demonstrating
that heat waves as commonly measured through the atmos-
pheric signal result in significant temperature anomalies
within compartments (rockwall, soil, lake), i.e., micro-
thermal anomalies. We also found great discrepancies
in the responses to the two summer heat waves that were
compared, and in responses across compartments. While
rockwalls and soils exhibited buffering capacity against
warm atmospheric temperature anomalies, lakes displayed
an amplified response, potentially accelerating the cross-
ing of ecological threshold. These findings highlight that
ecosystem responses to heat waves are heterogeneous and
compartment-specific, emphasizing the need for spatially
explicit models that can capture both buffering and ampli-
fying effects. The development, calibration, and evaluation
of such models depend on integrated, multi-compartment
measurements conducted during and outside extreme events, highlighting the critical role of coordinated environmental monitoring networks.

Understanding these microclimate dynamics has direct implications for ecosystem management and human activities in mountain areas. It can help anticipate impacts on biodiversity, water resources, and gravitational hazards, and guide adaptation of practices such as pastoralism and recreation. By linking high-frequency observations to ecosystem processes and potential tipping points, this work provides a framework to predict and mitigate the impacts of increasingly frequent and intense heat waves.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10113-025-02517-3.

**Acknowledgements** The authors would like to thank all the people involved in collecting data through the *PermaFrance*, *Orchamp*, *BIOCLIM*, *Lacs Sentinelles* observatory, and *RNT plans d'eau* long-term monitoring projects. Thanks are due to CREA Mont-Blanc and notably Anne Delestrade, who initiated the soil temperature monitoring program on several of the sites included in this study, in collaboration with individual volunteers (particularly Gérard Cordier, Patrick Magnin, and Alain Debourg) as well as several partners: ORANGE, SOMFY, ADISON, CECAM, and LCP. Other monitorings were launched and are supported by Parc National des Ecrins, Parc National du Mercantour, Parc National de la Vanoise, Asters-CEN74, *Lacs Sentinelles*, and *RNT plans d'eau* (Pôle R&D ECLA [OFB-INRAE-USMB], and specifically Tiphaine Péroux, Pierre-Alain Danis, and Philippe Moullec). The authors thank the Zone Atelier Alpes for funding one in-person meeting. The authors thank two anonymous reviewers for their constructive comments on an earlier version of this manuscript.

**Author contribution** All authors took part in the study conceptualisation. PC and CB secured the funding for the in-person workshop. FM curated the rockwall data and RB and FM computed the indicators for the rockwall compartment. PC, CS and CVR curated the soil data and RB and PC computed the indicators for the soil compartment. RN and RB curated the lake data, and RB, RN and FA computed the lake indicators. MF computed the atmospheric data for each site and RB computed the indicators. RB, FA and PC proposed the formal analysis which was discussed and validated in concertation with all of the authors. RB prepared the figures and ran the analysis. RB, FA and PC wrote the original draft, and all authors reviewed and approved the final version of the manuscript.

**Funding** Part of the funding was provided by the Parc National des Ecrins, Parc National de la Vanoise, Pôle R&D ECLA and OFB, by FEDER-POIA Lacs d'altitude alpins, ALCOTRA BIODIV'ALP, and by the French Ministry of Ecology for funding sensors as part of the "Plan d'Action pour les Risques d'Origine Glaciaire et Périglaciaire," OFB-DRAS, and EDF. This work received funding from the Agence Nationale de la Recherche (project TOP, grant no. ANR-20-CE32-0002). Part of this research was supported by the *Jardin Alpin du Lautaret*, a member of the eLTER Research Infrastructure. LECA acknowledges the Agence Nationale de la Recherche (grant nos. Labex OSUG@2020 and IA-10-LABX-0056).

**Data availability** Rockwall data are available through the GTN-P portal (https://gtnp.arcticportal.org/). Soil data are in open access and hosted under a Creative Commons license at https://entrepot.recherche.data.gouv.fr/dataverse/ZAA SOIL_TEMP and https://frostviewer.monitoring.creamontblanc.org. Lake data are stored in the

database bd-thermie hosted by Pôle ECLA (OFB-INRAE-USMB, Aix-en-Provence) and can be visualized through the dashboard "DATA-ECLA": https://dataecla.fr/ (https://dataecla.fr/dashboards/thermie/).

## Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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## Authors and Affiliations

Rosalie Bruel<sup>1,2,3 D</sup> · Florent Arthaud<sup>1,2,3 D</sup> · Florence Magnin<sup>4 D</sup> · Raphaëlle Napoleoni<sup>5</sup> · Colin Van Reeth<sup>6 D</sup> · Vincent Augé<sup>7</sup> · Clotilde Sagot<sup>8</sup> · Mathieu Fructus<sup>9 D</sup> · Carole Birck<sup>5</sup> · Philippe Choler<sup>10,11 D</sup>

<figure><img src="image_11.png" /></figure>

Rosalie Bruel
rosalie.bruel@ofb.gouv.fr

Florent Arthaud
florent.arthaud@univ-savoie.fr

Florence Magnin
Florence.Magnin@univ-smb.fr

Raphaëlle Napoleoni
raphaëlle.napoleoni@cen-haute-savoie.org

Colin Van Reeth
cvanreeth@creamontblanc.org

Vincent Augé
vincent.auge@vanoise-parcnational.fr

Clotilde Sagot
clotilde.sagot@ecrins-parcnational.fr

Mathieu Fructus
mathieu.fructus@meteo.fr

Carole Birck
carole.birck@cen-haute-savoie.org

Philippe Choler
philippe.choler@univ-grenoble-alpes.fr

<footnote n="1">¹ Service EcoAqua, Direction de la Recherche et de l'Appui Scientifique, OFB, Thonon-Les-Bains, France</footnote>

<footnote n="2">² Pôle R&D ECLA, Aix-en-Provence, France</footnote>

<footnote n="3">³ Univ. Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains, France</footnote>

<footnote n="4">⁴ Univ. Savoie Mont-Blanc, CNRS, EDYTEM, Le Bourget-du-Lac, France</footnote>

<footnote n="5">5 Asters-CEN74, Annecy, France</footnote>

<footnote n="6">⁶ CREA Mont-Blanc, Chamonix-Mont-Blanc, France</footnote>

<footnote n="7">7 Parc National de La Vanoise, Chambéry, France</footnote>

<footnote n="8">8 Parc National Des Ecrins, Gap, France</footnote>

<footnote n="9">9 Univ. Grenoble Alpes, Univ. Toulouse, Météo France, CNRS, CNRM, Centre d'Etudes de La Neige, Grenoble, France</footnote>

<footnote n="10">¹⁰ Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Grenoble, France</footnote>

<footnote n="11">¹¹ Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LTSER Zone Atelier Alpes, Grenoble, France</footnote>