# Projecting future forest microclimate using a land surface model

Gabriel Hes, Inne Vanderkelen, Rosie Fisher, Jérôme Chave, Jérôme Ogée,

Edouard L Davin

► To cite this version:

Gabriel Hes, Inne Vanderkelen, Rosie Fisher, Jérôme Chave, Jérôme Ogée, et al. Projecting future forest microclimate using a land surface model. Environmental Research Letters, 2024, 19 (2), pp.024030. 10.1088/1748-9326/ad1f04 hal-04467792

HAL Id: hal-04467792

https://hal.inrae.fr/hal-04467792

Submitted on 20 Feb 2024

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# Projecting future forest microclimate using a land surface model

Gabriel Hes, Inne Vanderkelen, Rosie Fisher, Jérôme Chave, Jérôme Ogée,

Edouard L Davin

► To cite this version:

Gabriel Hes, Inne Vanderkelen, Rosie Fisher, Jérôme Chave, Jérôme Ogée, et al. Projecting future forest microclimate using a land surface model. Environmental Research Letters, 2024, 19 (2), pp.024030. 10.1088/1748-9326/ad1f04 hal-04467792

HAL Id: hal-04467792

https://hal.inrae.fr/hal-04467792v1

Submitted on 20 Feb 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
LETTER • OPEN ACCESS

Projecting future forest microclimate using a land surface model

To cite this article: Gabriel Hes et al 2024 *Environ. Res. Lett.* **19** 024030

View the [article online](#) for updates and enhancements.

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# Projecting future forest microclimate using a land surface model

To cite this article: Gabriel Hes et al 2024 *Environ. Res. Lett.* **19** 024030

View the [article online](#) for updates and enhancements.

You may also like

- Current and future patterns of fire-induced forest degradation in *Amazonia*
Bruno L De Faria, Paulo M Brando, Marcia N Macedo et al.

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Thomas Dirnböck, Gisela Pröll, Kari Austnes et al.

- Climate change effects on understory plant phenology: implications for large herbivore forage availability
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This content was downloaded from IP address 147.100.179.233 on 20/02/2024 at 09:56
ENVIRONMENTAL RESEARCH
LETTERS

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CrossMark

OPEN ACCESS

RECEIVED

8 September 2023

REVISED

28 December 2023

ACCEPTED FOR PUBLICATION

16 January 2024

PUBLISHED

30 January 2024

Original Content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.

Any further distribution
of this work must
maintain attribution to
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of the work, journal
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LETTER

Projecting future forest microclimate using a land surface model

Gabriel Hes<fnref n="1" />,*, Inne Vanderkelen<fnref n="2" />,<fnref n="3" />,<fnref n="4" />, Rosie Fisher<fnref n="5" />, Jérôme Chave<fnref n="1" />, Jérôme Ogée<fnref n="6" /> and Edouard L Davin<fnref n="2" />,<fnref n="3" />,<fnref n="4" />,*

E-mail: gabriel.hes@univ-tlse3.fr and edouard.davin@unibe.ch

**Keywords:** Community Land Model, forest modeling, temperature buffering, tropical biodiversity, climate change, Land Surface Model, soil temperature

Abstract

The forest understory experiences temperature variations that are dampened compared to adjacent open areas, allowing the development of a forest microclimate and associated ecological conditions. It is however unclear to what extent forests will maintain this buffering effect under increasing global warming. Providing reliable projections of future forest microclimates is therefore crucial to anticipate climate change impacts on forest biodiversity, and to identify corresponding conservation strategies. Recent empirical studies suggest that the buffering of air temperature extremes in forest understory compared to open land could increase with global warming, albeit at a slower rate than macroclimate temperatures. Here, we investigate the trend of this temperature buffering effect in a high-emission global warming scenario, using the process-based Land Surface Model CLM5.1. We find biome-dependant buffering trends with strongest values in tropical forests where buffering increases for every degree of global warming by 0.1 °C for maximum soil temperature, and by 0.2 °C for maximum canopy air temperature. In boreal regions, forest microclimate exhibits a strong seasonality and the effect of global warming is more uncertain. Thus, our results highlight the importance of tropical forest canopies in particular, in maintaining hospitable conditions for understory species while increasing their climate debt under global warming. Our research also illustrates the potential and limitations of Land Surface Models to simulate forest microclimate, and calls for further collaborations between Earth system modelers and ecologists to jointly question climate and biosphere dynamics.

**1. Introduction**

Forests play an important role in shaping climate conditions from local to global scales by mediating greenhouse gas, water and energy fluxes between the land and the atmosphere (Davin and Noblet-Ducoudré 2010, Teuling *et al* 2017, O’Connor *et al* 2021, Roebroek *et al* 2023). In addition, tree canopies create distinct climatic conditions in the forest understory, which are typically referred to as the forest microclimate (Geiger *et al* 2009). Tree canopies act as thermal insulators, which buffer understory temperature variations (von Arx *et al* 2012, De Frenne *et al*

2019, Meier et al 2019). Indeed understory temperat-
ures (T_in) are usually cooler than outside-forest tem-
peratures (T_out) under hot conditions and warmer
under cold conditions, a phenomenon known as
the ‘buffering effect’, and thus typically reduce the
diurnal or seasonal amplitude of temperature vari-
ations (Geiger et al 2009, Von Arx et al 2013, Li
et al 2015, De Frenne et al 2019). However, the
opposite phenomenon -an amplification effect- is
also observed in specific forest environments such
as open canopy mountain forests (Vandewiele et al
2023). A common metric to assess both effects is the
difference between within-forest temperature (forest

<footnote n="1">¹ Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 - Paul Sabatier (UT3), Toulouse, France</footnote>

<footnote n="2">² Wyss Academy for Nature, University of Bern, Bern, Switzerland</footnote>

<footnote n="3">³ Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland</footnote>

<footnote n="4">⁴ Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland</footnote>

<footnote n="5">5 CICERO Center for International Climate Research, Oslo, Norway</footnote>

<footnote n="6">⁶ Atmosphere Plant Soil Interactions Research Unit (UMR ISPA), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), 71 Av. Edouard Bourlaux, F-33140 Villenave d'Ornon, France</footnote>

<footnote>* Authors to whom any correspondence should be addressed.</footnote>
ENVIRONMENTAL RESEARCH
LETTERS

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CrossMark

OPEN ACCESS

RECEIVED

8 September 2023

REVISED

28 December 2023

ACCEPTED FOR PUBLICATION

16 January 2024

PUBLISHED

30 January 2024

Original Content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.

Any further distribution
of this work must
maintain attribution to
the author(s) and the title
of the work, journal
citation and DOI.

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LETTER

Projecting future forest microclimate using a land surface model

Gabriel Hes<fnref n="1" />,*, Inne Vanderkelen<fnref n="2" />,<fnref n="3" />,<fnref n="4" />, Rosie Fisher<fnref n="5" />, Jérôme Chave<fnref n="1" />, Jérôme Ogée<fnref n="6" /> and Edouard L Davin<fnref n="2" />,<fnref n="3" />,<fnref n="4" />,*

E-mail: gabriel.hes@univ-tlse3.fr and edouard.davin@unibe.ch

**Keywords:** Community Land Model, forest modeling, temperature buffering, tropical biodiversity, climate change, Land Surface Model, soil temperature

Abstract

The forest understory experiences temperature variations that are dampened compared to adjacent open areas, allowing the development of a forest microclimate and associated ecological conditions. It is however unclear to what extent forests will maintain this buffering effect under increasing global warming. Providing reliable projections of future forest microclimates is therefore crucial to anticipate climate change impacts on forest biodiversity, and to identify corresponding conservation strategies. Recent empirical studies suggest that the buffering of air temperature extremes in forest understory compared to open land could increase with global warming, albeit at a slower rate than macroclimate temperatures. Here, we investigate the trend of this temperature buffering effect in a high-emission global warming scenario, using the process-based Land Surface Model CLM5.1. We find biome-dependant buffering trends with strongest values in tropical forests where buffering increases for every degree of global warming by 0.1 °C for maximum soil temperature, and by 0.2 °C for maximum canopy air temperature. In boreal regions, forest microclimate exhibits a strong seasonality and the effect of global warming is more uncertain. Thus, our results highlight the importance of tropical forest canopies in particular, in maintaining hospitable conditions for understory species while increasing their climate debt under global warming. Our research also illustrates the potential and limitations of Land Surface Models to simulate forest microclimate, and calls for further collaborations between Earth system modelers and ecologists to jointly question climate and biosphere dynamics.

**1. Introduction**

Forests play an important role in shaping climate conditions from local to global scales by mediating greenhouse gas, water and energy fluxes between the land and the atmosphere (Davin and Noblet-Ducoudré 2010, Teuling *et al* 2017, O’Connor *et al* 2021, Roebroek *et al* 2023). In addition, tree canopies create distinct climatic conditions in the forest understory, which are typically referred to as the forest microclimate (Geiger *et al* 2009). Tree canopies act as thermal insulators, which buffer understory temperature variations (von Arx *et al* 2012, De Frenne *et al*

2019, Meier et al 2019). Indeed understory temperat-
ures (T_in) are usually cooler than outside-forest tem-
peratures (T_out) under hot conditions and warmer
under cold conditions, a phenomenon known as
the ‘buffering effect’, and thus typically reduce the
diurnal or seasonal amplitude of temperature vari-
ations (Geiger et al 2009, Von Arx et al 2013, Li
et al 2015, De Frenne et al 2019). However, the
opposite phenomenon -an amplification effect- is
also observed in specific forest environments such
as open canopy mountain forests (Vandewiele et al
2023). A common metric to assess both effects is the
difference between within-forest temperature (forest

<footnote n="1">¹ Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 - Paul Sabatier (UT3), Toulouse, France</footnote>

<footnote n="2">² Wyss Academy for Nature, University of Bern, Bern, Switzerland</footnote>

<footnote n="3">³ Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland</footnote>

<footnote n="4">⁴ Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland</footnote>

<footnote n="5">5 CICERO Center for International Climate Research, Oslo, Norway</footnote>

<footnote n="6">⁶ Atmosphere Plant Soil Interactions Research Unit (UMR ISPA), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), 71 Av. Edouard Bourlaux, F-33140 Villenave d'Ornon, France</footnote>

<footnote>* Authors to whom any correspondence should be addressed.</footnote>
microclimate) and outside-forest temperature (macroclimate) defined as $\Delta T = T_{in} - T_{out}$ (De Frenne et al 2019). This temperature offset has been shown to correlate negatively with the mean macroclimate temperature across biomes and to increase in magnitude with decreasing latitudes (De Frenne et al 2019).

Temperature extremes strongly affect biodiversity by altering species' physiological performances (Huey et al 2009, Vasseur et al 2014). In that respect, forest microclimates act as climatic refugia (i.e. hospitable habitats) for forest understory species (Keppel et al 2012, Hannah et al 2014). Over the past decades it has been shown that such species respond more closely to microclimate variations than macroclimate warming (De Frenne et al 2013, Williamson et al 2020, Zellweger et al 2020, De Pauw et al 2022). To date, we observe that the discrepancy between forest microclimate and regional climate often delays species migration or local extinction compared to what we would expect from macroclimate warming (Lenoir et al 2017), a process often coined as 'climatic debt' (Bertrand et al 2016, Richard et al 2021). However, as stronger and more frequent extreme temperatures are expected with climate change (IPCC 2021), we do not know if forest understories can serve as long-term refugia for species, or, in other words, how large of a climatic debt can species accumulate before being locally extinct (De Frenne et al 2021, Richard et al 2021). To address this question, one must first understand how stable the temperature decoupling in forests (inside vs. outside) is under global climate warming (Lenoir et al 2017, De Frenne et al 2019, 2021, Lembrechts and Nijs 2020). In particular, determining the evolution of tropical forest microclimates is of utmost importance given that at least 2/3 of global species lie within tropical forest ecosystems (Pillay et al 2022). In that respect, better understanding biotic and abiotic drivers of forest microclimate is key to anticipating the response of forest biodiversity to global warming (Frey et al 2016, De Frenne et al 2021).

The growing number of monitored forest sites allows assessment of the relations between species distribution, forest structure and microclimate, but such efforts are strongly limited by the scarcity of microclimate data (De Frenne et al 2021). Analysis of the available data shows that forest temperature offsets correlate negatively with outside forest mean temperature (i.e. forest buffering generally decreases with latitude) (De Frenne et al 2019). In particular, we observe that tropical forests are strongly buffered even for small macroclimate temperature increases (Senior et al 2018). However, we currently lack long time-series required to provide robust evidence of the impact of a substantial rise in global mean temperatures on forest microclimate (Lembrechts and Nijs 2020, Sanczuk et al 2023). It is yet unclear whether

the magnitude of the temperature offset will remain stable, increase, or decrease over time as macroclimate warms (De Frenne et al 2021). De Lombaerde et al (2022) addressed this question through a statistical modeling approach. Macroclimate was found to be a major driver of the observed temperature offset across multiple forest sites. The associated statistical relationship was used to generate future projections of temperature offsets globally. Assuming a constant forest cover, the warming rate of the forest understory was forecast to be slower than that over non-forested areas in a global warming scenario. Such a prediction is consistent with the observation of a higher buffering in warmer biomes. The question we address in this study is whether this space-for-time statistical approach is appropriate to forecast warming rates of understory microclimate across different biomes. For instance, in their study, De Lombaerde et al (2022) assume that the statistical relationship between macroclimate and microclimate is constant, irrespective of the warming conditions.

Because large-scale experimental warming of forest is neither feasible nor desirable, estimation of how forest microclimate will be altered in a changing climate requires the use of models encapsulating our mechanistic understanding of the physical controls over land-atmosphere interactions, energy budgets and gas exchange. Here we use a process-based global land surface model (CLM5.1) to produce estimates of the microclimatic buffering effect. CLM5.1 represents the forest understory microclimate via the differentiation between the forest soil temperature and the grassland soil temperature. Similarly, the canopy microclimate buffering is represented here as the differentiation between the forest canopy air temperature and the grassland canopy air temperature. Although several multilayer canopy models (CLM-ml (Bonan et al 2018), ORCHIDE-CAN (Ryder et al 2016), MuSICA (Ogée et al 2003), microclimC (Maclean and Klinges 2021)) represent forest energy and hydrology budgets more accurately (Bonan et al 2021), none is currently operational in a global modeling system and they have primarily been used in single point evaluations. CLM5.1 can be run globally with moderate resolution (0.5°) and in addition, was recently modified to include a biomass heat storage scheme, generating more realistic diurnal temperature variations in vegetated areas (Meier et al 2019). Land model simulations provide a globally consistent representation of forests while observational studies are confronted to data scarcity for specific biomes. For instance, the SoilTemp database (Lembrechts et al 2020) currently lacks data over the boreal region.

In this article, we investigate the global change of forest-induced soil and canopy air temperature over historical and future periods. Our main hypothesis is that there is an increasing buffering effect under
microclimate) and outside-forest temperature (macroclimate) defined as $\Delta T = T_{in} - T_{out}$ (De Frenne et al 2019). This temperature offset has been shown to correlate negatively with the mean macroclimate temperature across biomes and to increase in magnitude with decreasing latitudes (De Frenne et al 2019).

Temperature extremes strongly affect biodiversity by altering species' physiological performances (Huey et al 2009, Vasseur et al 2014). In that respect, forest microclimates act as climatic refugia (i.e. hospitable habitats) for forest understory species (Keppel et al 2012, Hannah et al 2014). Over the past decades it has been shown that such species respond more closely to microclimate variations than macroclimate warming (De Frenne et al 2013, Williamson et al 2020, Zellweger et al 2020, De Pauw et al 2022). To date, we observe that the discrepancy between forest microclimate and regional climate often delays species migration or local extinction compared to what we would expect from macroclimate warming (Lenoir et al 2017), a process often coined as 'climatic debt' (Bertrand et al 2016, Richard et al 2021). However, as stronger and more frequent extreme temperatures are expected with climate change (IPCC 2021), we do not know if forest understories can serve as long-term refugia for species, or, in other words, how large of a climatic debt can species accumulate before being locally extinct (De Frenne et al 2021, Richard et al 2021). To address this question, one must first understand how stable the temperature decoupling in forests (inside vs. outside) is under global climate warming (Lenoir et al 2017, De Frenne et al 2019, 2021, Lembrechts and Nijs 2020). In particular, determining the evolution of tropical forest microclimates is of utmost importance given that at least 2/3 of global species lie within tropical forest ecosystems (Pillay et al 2022). In that respect, better understanding biotic and abiotic drivers of forest microclimate is key to anticipating the response of forest biodiversity to global warming (Frey et al 2016, De Frenne et al 2021).

The growing number of monitored forest sites allows assessment of the relations between species distribution, forest structure and microclimate, but such efforts are strongly limited by the scarcity of microclimate data (De Frenne et al 2021). Analysis of the available data shows that forest temperature offsets correlate negatively with outside forest mean temperature (i.e. forest buffering generally decreases with latitude) (De Frenne et al 2019). In particular, we observe that tropical forests are strongly buffered even for small macroclimate temperature increases (Senior et al 2018). However, we currently lack long time-series required to provide robust evidence of the impact of a substantial rise in global mean temperatures on forest microclimate (Lembrechts and Nijs 2020, Sanczuk et al 2023). It is yet unclear whether

the magnitude of the temperature offset will remain stable, increase, or decrease over time as macroclimate warms (De Frenne et al 2021). De Lombaerde et al (2022) addressed this question through a statistical modeling approach. Macroclimate was found to be a major driver of the observed temperature offset across multiple forest sites. The associated statistical relationship was used to generate future projections of temperature offsets globally. Assuming a constant forest cover, the warming rate of the forest understory was forecast to be slower than that over non-forested areas in a global warming scenario. Such a prediction is consistent with the observation of a higher buffering in warmer biomes. The question we address in this study is whether this space-for-time statistical approach is appropriate to forecast warming rates of understory microclimate across different biomes. For instance, in their study, De Lombaerde et al (2022) assume that the statistical relationship between macroclimate and microclimate is constant, irrespective of the warming conditions.

Because large-scale experimental warming of forest is neither feasible nor desirable, estimation of how forest microclimate will be altered in a changing climate requires the use of models encapsulating our mechanistic understanding of the physical controls over land-atmosphere interactions, energy budgets and gas exchange. Here we use a process-based global land surface model (CLM5.1) to produce estimates of the microclimatic buffering effect. CLM5.1 represents the forest understory microclimate via the differentiation between the forest soil temperature and the grassland soil temperature. Similarly, the canopy microclimate buffering is represented here as the differentiation between the forest canopy air temperature and the grassland canopy air temperature. Although several multilayer canopy models (CLM-ml (Bonan et al 2018), ORCHIDE-CAN (Ryder et al 2016), MuSICA (Ogée et al 2003), microclimC (Maclean and Klinges 2021)) represent forest energy and hydrology budgets more accurately (Bonan et al 2021), none is currently operational in a global modeling system and they have primarily been used in single point evaluations. CLM5.1 can be run globally with moderate resolution (0.5°) and in addition, was recently modified to include a biomass heat storage scheme, generating more realistic diurnal temperature variations in vegetated areas (Meier et al 2019). Land model simulations provide a globally consistent representation of forests while observational studies are confronted to data scarcity for specific biomes. For instance, the SoilTemp database (Lembrechts et al 2020) currently lacks data over the boreal region.

In this article, we investigate the global change of forest-induced soil and canopy air temperature over historical and future periods. Our main hypothesis is that there is an increasing buffering effect under
forest canopies with global warming, as suggested by De Lombaerde et al. (2022). To test this hypothesis in the presence of large changes in temperature, we use the highest IPCC warming scenario (SSP5-8.5) and focus on minimum and maximum temperatures, assuming that a trend in the offset signal would be more visible in this scenario. We first analyse global historical and future patterns of simulated temperature offsets. We then provide estimations of future offsets per degree of global warming. Finally, we discuss the implications of future temperature buffering on forest biodiversity and the limits of using a single layer Land Surface Model such as CLM5.1 to represent within-forest temperature variations.

## 2. Methods

### 2.1. Model description

To investigate the effect of climate change on global forest temperatures, we used the land component CLM (Community Land Model) of the Community Earth System Model (CESM2, version 2.1.2, Danabasoglu et al. (2020)), a state-of-the-art and open-source Earth System Model. CLM version 5.1 (CLM5.1) is an advanced land surface model, representing biophysical and biochemical processes related to the Earth surface (Lawrence et al. 2019). The model accounts for surface heterogeneity through a nested-subgrid hierarchy. Each gridcell is divided into land units, representing different land uses, which are composed of one or multiple soil columns (Lawrence et al. 2019). For vegetated land units, the soil column is subdivided into a maximum of 15 different plant functional type (PFT) patches and bare ground, which all share the same column state and compete for soil water. The state variables and fluxes for water and energy are defined at the column level as weighted averages over all PFTs within the column. Here we used a reduced complexity version of the model (satellite phenology, SP mode) that disables biogeochemical processes and instead is driven by a constant seasonal cycle for leaf and stem area indices (LAI and SAI) and by prescribed canopy height for each PFT patch. The SP mode allows a focus on biophysical processes that are not subject to biases introduced via uncertain simulation of plant growth and competition.

CLM5.1 features a homogeneous single layer representation of the plant canopy (also known as the 'big leaf approach') with no internal vertical structure (Bonan et al. 2021). This simplified representation of the forest canopy is complemented by recent developments of the biomass heat storage scheme by Meier et al. (2019) and Swenson et al. (2019), resulting in more dampened diurnal land surface temperature profiles in forests that better represents observed diurnal temperature profiles. Based on PFT-specific

thermal properties, stems and leaves store and radiate heat, which generates a lag in air temperature variations. Finally, we used the novel surface roughness parametrization described in Meier et al. (2022) for improved temperature and wind speed profiles above forested regions.

### 2.2. Experimental design

We performed two land-only simulations with CLM5.1 in SP mode at 0.5° by 0.5° resolution which together cover the 21st century (1995–2099). Before starting the first simulation, we ran the model over the 1990–1994 period (the spin-up phase) in order to reach a state of statistical equilibrium under the applied forcing. The spin-up of the full CLM5.1 model typically takes hundreds of years, but for SP mode this is not necessary because the carbon pools in this mode are prescribed and not prognostic, and thus only the physical state of the model (energy and water) needs to achieve an equilibrium. The first simulation is a historical run covering the 1995–2014 period for which we use the CLM5.1 default GSWPv3 reanalysis (Kim et al. 2017) as atmospheric forcing. The second simulation is branched from the historical one and consists of a future SSP8.5 high emissions scenario over 2015–2099 (appendix figure B1). The required future atmospheric forcing was obtained by applying climate anomalies of a previous CESM simulation to the GSWPv3 2001–2013 climatology (Lawrence et al. 2020). This offline approach allowed to focus on the warming effects on land (rather than the feedbacks on the atmosphere) and avoids running computationally-intensive coupled land-atmosphere simulations.

Both simulations employ the same static land-use map, omitting land use changes. The distribution of PFTs is based on MODIS satellite data (Lawrence and Chase 2007, Lawrence et al. 2020). Monthly prescribed LAI and SAI are also derived from MODIS data (Myneni et al. 2002) following the methods described in Lawrence and Chase (2007) and Zeng et al. (2002), respectively. The fixed canopy top and bottom heights for trees are retrieved from the Geoscience Laser Altimeter System (GLAS) aboard the ICESat satellite and are PFT-independent (Simard et al. 2011), while their counterparts for short vegetation are PFT-specific (Bonan et al. 2002, Lawrence et al. 2020). By ignoring future changes in land use or vegetation dynamics we can focus in this analysis on quantifying the evolution of forest microclimates under increasing warming independently of potential changes in future forest distribution and structure.

### 2.3. Model analysis and evaluation

In field studies, the forest microclimate is typically quantified using a wide range of temperature variables (soil temperature, 1 m air temperature or
forest canopies with global warming, as suggested by De Lombaerde et al (2022). To test this hypothesis in the presence of large changes in temperature, we use the highest IPCC warming scenario (SSP5-8.5) and focus on minimum and maximum temperatures, assuming that a trend in the offset signal would be more visible in this scenario. We first analyse global historical and future patterns of simulated temperature offsets. We then provide estimations of future offsets per degree of global warming. Finally, we discuss the implications of future temperature buffering on forest biodiversity and the limits of using a single layer Land Surface Model such as CLM5.1 to represent within-forest temperature variations.

## 2. Methods

### 2.1. Model description

To investigate the effect of climate change on global forest temperatures, we used the land component CLM (Community Land Model) of the Community Earth System Model (CESM2, version 2.1.2, Danabasoglu et al (2020)), a state-of-the-art and open-source Earth System Model. CLM version 5.1 (CLM5.1) is an advanced land surface model, representing biophysical and biochemical processes related to the Earth surface (Lawrence et al 2019). The model accounts for surface heterogeneity through a nested-subgrid hierarchy. Each gridcell is divided into land units, representing different land uses, which are composed of one or multiple soil columns (Lawrence et al 2019). For vegetated land units, the soil column is subdivided into a maximum of 15 different plant functional type (PFT) patches and bare ground, which all share the same column state and compete for soil water. The state variables and fluxes for water and energy are defined at the column level as weighted averages over all PFTs within the column. Here we used a reduced complexity version of the model (satellite phenology, SP mode) that disables biogeochemical processes and instead is driven by a constant seasonal cycle for leaf and stem area indices (LAI and SAI) and by prescribed canopy height for each PFT patch. The SP mode allows a focus on biophysical processes that are not subject to biases introduced via uncertain simulation of plant growth and competition.

CLM5.1 features a homogeneous single layer representation of the plant canopy (also known as the 'big leaf approach') with no internal vertical structure (Bonan et al 2021). This simplified representation of the forest canopy is complemented by recent developments of the biomass heat storage scheme by Meier et al (2019) and Swenson et al (2019), resulting in more dampened diurnal land surface temperature profiles in forests that better represents observed diurnal temperature profiles. Based on PFT-specific

thermal properties, stems and leaves store and radiate heat, which generates a lag in air temperature variations. Finally, we used the novel surface roughness parametrization described in Meier et al (2022) for improved temperature and wind speed profiles above forested regions.

### 2.2. Experimental design

We performed two land-only simulations with CLM5.1 in SP mode at 0.5° by 0.5° resolution which together cover the 21st century (1995–2099). Before starting the first simulation, we ran the model over the 1990–1994 period (the spin-up phase) in order to reach a state of statistical equilibrium under the applied forcing. The spin-up of the full CLM5.1 model typically takes hundreds of years, but for SP mode this is not necessary because the carbon pools in this mode are prescribed and not prognostic, and thus only the physical state of the model (energy and water) needs to achieve an equilibrium. The first simulation is a historical run covering the 1995–2014 period for which we use the CLM5.1 default GSWPv3 reanalysis (Kim et al 2017) as atmospheric forcing. The second simulation is branched from the historical one and consists of a future SSP8.5 high emissions scenario over 2015–2099 (appendix figure B1). The required future atmospheric forcing was obtained by applying climate anomalies of a previous CESM simulation to the GSWPv3 2001–2013 climatology (Lawrence et al 2020). This offline approach allowed to focus on the warming effects on land (rather than the feedbacks on the atmosphere) and avoids running computationally-intensive coupled land-atmosphere simulations.

Both simulations employ the same static land-use map, omitting land use changes. The distribution of PFTs is based on MODIS satellite data (Lawrence and Chase 2007, Lawrence et al 2020). Monthly prescribed LAI and SAI are also derived from MODIS data (Myneni et al 2002) following the methods described in Lawrence and Chase (2007) and Zeng et al (2002), respectively. The fixed canopy top and bottom heights for trees are retrieved from the Geoscience Laser Altimeter System (GLAS) aboard the ICESat satellite and are PFT-independent (Simard et al 2011), while their counterparts for short vegetation are PFT-specific (Bonan et al 2002, Lawrence et al 2020). By ignoring future changes in land use or vegetation dynamics we can focus in this analysis on quantifying the evolution of forest microclimates under increasing warming independently of potential changes in future forest distribution and structure.

### 2.3. Model analysis and evaluation

In field studies, the forest microclimate is typically quantified using a wide range of temperature variables (soil temperature, 1 m air temperature or
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Temperature representation across different PFTs in CLM5.1. The canopy air temperature ($T_c$) and the surface air temperature (TSA) are a function of displacement height (d) which is specific to each PFT whereas the top 10 cm averaged soil temperature ($T_g$) is independent from the displacement height. TBOT is the lowest atmospheric temperature level forcing the land surface model. The canopy height (shaded green) is defined as the difference between canopy top height and bottom height. While $T_g$ is affected by a snow cover-induced insulation in forests and grasslands at high latitude, $T_c$ is only impacted by this effect over grasslands where the canopy can be covered by snow during parts of the year. Tree and grassland PFTs share the same soil column and associated water stock.</figcaption></figure>

canopy air temperature), depending on the technical feasibility and the research question. Given the single-layer canopy representation in CLM5.1, an estimated 2 m air temperature (TSA) was provided by interpolating between the atmospheric temperature given by the atmospheric forcing, and the surface temperature simulated in the model. Therefore, this 2 m air temperature diagnostic does not represent the shading or buffering effect of vegetation canopies, as also shown in Malyshev *et al* (2015). On the contrary, the soil temperature ($T_g$, °C) -the temperature average over the first 10 cm of soil- and the canopy air temperature ($T_c$, °C) -the surface air temperature defined within vegetation canopies- better represent the temperatures under and within the canopy respectively. $T_g$ is calculated by solving the one-dimensional heat equation over the first soil layer and $T_c$ is defined as a weighted mean of the atmospheric potential temperature, the vegetation temperature and the soil temperature (see appendix C.1). $T_c$ depends on canopy height and represents the air temperature below the canopy top in forests and above canopy top over grasslands (figure 1). Therefore, $T_c$ inside and outside forests is not at the same physical height. This has important implications at temperate and boreal latitudes where grass can be buried under snow during part of the year. In such cases, $T_c$ is close to the temperature of the snow surface (Lawrence *et al* 2018). In contrast, the top soil layer $T_g$ does not depend on canopy height and is thus simulated at the same physical height for every PFT. Therefore, it is subject to the same snow cover conditions over forest and grassland. In addition, most biodiversity inventories are

performed near the soil surface but future projections of soil temperature are typically lacking (Lembrechts *et al* 2022). For the boreal biome in particular, the soil environment below the snow cover is a key refuge for many organisms (Niittynen *et al* 2018, Kayes and Mallik 2020). Therefore, this paper focuses on $T_g$ to provide the most relevant temperature projections for species dynamics. While $T_g$ is probably the best CLM5.1 proxy for near soil air temperature within forests, additional factors such as heat capacity, soil moisture and snow cover dynamics can result in a decoupling of soil and air temperature (Lembrechts *et al* 2022). In this perspective, we also provide a complementary analysis for canopy temperature in the appendix C.1, to serve as a comparison in modelled responses and to discuss model development pathways for below canopy temperature representations.

Observations of forest microclimate are commonly expressed as a temperature difference between inside forest and outside forest environments that are subject to similar atmospheric forcings (De Frenne *et al* 2019, Lembrechts *et al* 2020). Likewise, we constructed a modeled offset temperature $\Delta T$ defined as the difference between the internal temperature of the forest tile minus the internal temperature of the grassland tile. For each grid cell, we computed the difference between the area-weighted average temperature in forests, and the area-weighted average temperature over grassland. Forest and grass areas were derived per the PFT distribution map. The 9 forest and 3 grassland PFTs are listed in appendix table A1.

Similar to De Lombaerde *et al* (2022), we defined the offset for minimum $\Delta T_{g,min}$, maximum $\Delta T_{g,max}$
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Temperature representation across different PFTs in CLM5.1. The canopy air temperature ($T_c$) and the surface air temperature (TSA) are a function of displacement height (d) which is specific to each PFT whereas the top 10 cm averaged soil temperature ($T_g$) is independent from the displacement height. TBOT is the lowest atmospheric temperature level forcing the land surface model. The canopy height (shaded green) is defined as the difference between canopy top height and bottom height. While $T_g$ is affected by a snow cover-induced insulation in forests and grasslands at high latitude, $T_c$ is only impacted by this effect over grasslands where the canopy can be covered by snow during parts of the year. Tree and grassland PFTs share the same soil column and associated water stock.</figcaption></figure>

canopy air temperature), depending on the technical feasibility and the research question. Given the single-layer canopy representation in CLM5.1, an estimated 2 m air temperature (TSA) was provided by interpolating between the atmospheric temperature given by the atmospheric forcing, and the surface temperature simulated in the model. Therefore, this 2 m air temperature diagnostic does not represent the shading or buffering effect of vegetation canopies, as also shown in Malyshev *et al* (2015). On the contrary, the soil temperature ($T_g$, °C) -the temperature average over the first 10 cm of soil- and the canopy air temperature ($T_c$, °C) -the surface air temperature defined within vegetation canopies- better represent the temperatures under and within the canopy respectively. $T_g$ is calculated by solving the one-dimensional heat equation over the first soil layer and $T_c$ is defined as a weighted mean of the atmospheric potential temperature, the vegetation temperature and the soil temperature (see appendix C.1). $T_c$ depends on canopy height and represents the air temperature below the canopy top in forests and above canopy top over grasslands (figure 1). Therefore, $T_c$ inside and outside forests is not at the same physical height. This has important implications at temperate and boreal latitudes where grass can be buried under snow during part of the year. In such cases, $T_c$ is close to the temperature of the snow surface (Lawrence *et al* 2018). In contrast, the top soil layer $T_g$ does not depend on canopy height and is thus simulated at the same physical height for every PFT. Therefore, it is subject to the same snow cover conditions over forest and grassland. In addition, most biodiversity inventories are

performed near the soil surface but future projections of soil temperature are typically lacking (Lembrechts *et al* 2022). For the boreal biome in particular, the soil environment below the snow cover is a key refuge for many organisms (Niittynen *et al* 2018, Kayes and Mallik 2020). Therefore, this paper focuses on $T_g$ to provide the most relevant temperature projections for species dynamics. While $T_g$ is probably the best CLM5.1 proxy for near soil air temperature within forests, additional factors such as heat capacity, soil moisture and snow cover dynamics can result in a decoupling of soil and air temperature (Lembrechts *et al* 2022). In this perspective, we also provide a complementary analysis for canopy temperature in the appendix C.1, to serve as a comparison in modelled responses and to discuss model development pathways for below canopy temperature representations.

Observations of forest microclimate are commonly expressed as a temperature difference between inside forest and outside forest environments that are subject to similar atmospheric forcings (De Frenne *et al* 2019, Lembrechts *et al* 2020). Likewise, we constructed a modeled offset temperature $\Delta T$ defined as the difference between the internal temperature of the forest tile minus the internal temperature of the grassland tile. For each grid cell, we computed the difference between the area-weighted average temperature in forests, and the area-weighted average temperature over grassland. Forest and grass areas were derived per the PFT distribution map. The 9 forest and 3 grassland PFTs are listed in appendix table A1.

Similar to De Lombaerde *et al* (2022), we defined the offset for minimum $\Delta T_{g,min}$, maximum $\Delta T_{g,max}$
and mean $\Delta T_{g,mean}$ soil temperature. $\Delta T_{g,min}$ (respectively, $\Delta T_{g,mean}$ and $\Delta T_{g,max}$) was derived by selecting the monthly minimum (respectively, mean and maximum) of the 6-hourly forest $T_g$ and subtracting the simultaneous $T_g$ over grassland within a grid cell. The distinction between forest, grassland and other vegetation types is possible through the subgrid tiling of CLM5.1, which allows to directly write the output per PFT, corresponding to the level at which the computations are conducted before being averaged at the grid cell level (Lawrence et al 2019). Thus, we considered all grid cells that contain both more than 0% of tree PFT cover (forest) and more than 0% of grass PFT cover (grassland). $T_c$ offsets ($\Delta T_{c,min}$, $\Delta T_{c,max}$ and $\Delta T_{c,mean}$) were generated using the same method although we note that unlike for $T_g$, the differentiation between forest and grassland $T_c$ also encapsulates a difference in height. In order to evaluate the effects of global warming on forest temperature offsets, we compared the historical climatology (1995–2014) to the future climatology (2080–2099). We focused our analysis on three major biomes (boreal, temperate and tropical) defined by the grid cells containing the corresponding tree PFTs (table A1). Geographically speaking, these categories roughly correspond to 3 latitudinal bands: between 50° and 70° for the boreal forest, 25° and 50° for the temperate forest and 0° and 25° for the tropical forest as shown on figure A1.

Finally, we constructed a global mean surface temperature anomaly relative to the 1850–1899 average ($\Delta GMT$), based on HadCrut5 observational data (Morice et al 2021). This allows to assess soil and canopy air temperature offsets per degree of global warming.

# 3. Results

## 3.1. Annual mean offset patterns

The annual average soil temperature offset (negative when the soil surface is cooler below the forest canopy compared to below the grassland, and positive otherwise) over the historical period features a pronounced latitudinal gradient, but with opposite signs for maximum and minimum soil temperature (figure 2). $\Delta T_{g,max}$ increases with latitude, from very negative values (–6 °C) in tropical regions to slightly positive values (+0.5 °C) in boreal regions indicating that tropical forests have a consistent buffering effect on forest understory maximum soil temperature whereas some boreal forests have a small amplifying effect. $\Delta T_{g,mean}$ shows a similar but smaller gradient (ranging between –2 and +0.5 °C) compared to $\Delta T_{g,max}$. $\Delta T_{c,min}$ was found to decrease with latitude, from slightly positive values (+1 °C) in the tropics to slightly negative values in the boreal region

(–2 °C), implying a buffering of minimum $T_g$ in tropical forests and an amplification in boreal forests. Overall, canopy air temperature offsets show similar latitudinal gradients during the historical period (figure E1), albeit with larger magnitudes, especially in boreal regions for which minimum $T_c$ and maximum $T_c$ are strongly amplified. However, $T_c$ and $T_g$ offsets differ in the tropics and subtropics where only maxima are dampened for $T_c$ (figures E1(g) and (i)) whereas both extremes are systematically buffered for $T_g$ (figures 2(g) and (i)).

Next, we compared the future period (2080–2099) forced by the high emission SSP5-8.5 scenario (~+4.4 K in global mean surface temperature, (IPCC 2021)), to the historical one (period 1850–1900). We calculated the difference between the temperature offset in the future relative to the historical baseline (figures 2(d)–(f)).

We found a significant negative difference for maximum $T_g$ in the tropics, suggesting that under high warming levels, tropical forests increase their buffering capacity for high temperature extremes by an average of 0.55 °C. This result is underlined by the shift in the distribution of tropical $\Delta T_{g,max}$ figure 2(g). In addition, a small shift of $\Delta T_{g,min}$ towards a zero-centered distribution in boreal forests implies less amplification of minimum soil temperatures during the future period.

Future changes for canopy air temperature offsets (figure E1) are qualitatively similar but with even larger amplitudes than for soil temperatures. In tropical forests, we observe that $\Delta T_{c,max}$ is buffered by 1.16 °C in the warmer period compared to the past period. We also found an increase of buffering capacities for boreal forests, decreasing the warming effect by ~0.43 °C (figure E1(g)). Finally, $\Delta T_{c,min}$ remains very small in the future over the tropical and temperate regions, but increases significantly at boreal latitudes (~0.34 °C change) while staying overall negative. Most boreal regions show a pronounced increase in temperature offset (over 3 °C on the Labrador peninsula), with forests further increasing minimum temperatures in the future, while Eastern Siberia shows negative values (figure E1(f)). This pattern reveals a contrasting biophysical response across the boreal region at higher warming levels with an overall reduced minimum $T_c$ amplification (the minimum temperature offset is less negative in the future) opposed to an increased minimum $T_c$ amplification over Siberia. However, this boreal dipole pattern is not visible on the $\Delta T_{g,min}$ future map (figure 2(f)), suggesting that it results from tree-atmosphere interactions rather than soil-tree interactions.

## 3.2. Seasonal offset patterns

Exploring the seasonality of temperature offsets, we find that annual patterns mask out a seasonal
and mean $\Delta T_{g,mean}$ soil temperature. $\Delta T_{g,min}$ (respectively, $\Delta T_{g,mean}$ and $\Delta T_{g,max}$) was derived by selecting the monthly minimum (respectively, mean and maximum) of the 6-hourly forest $T_g$ and subtracting the simultaneous $T_g$ over grassland within a grid cell. The distinction between forest, grassland and other vegetation types is possible through the subgrid tiling of CLM5.1, which allows to directly write the output per PFT, corresponding to the level at which the computations are conducted before being averaged at the grid cell level (Lawrence et al 2019). Thus, we considered all grid cells that contain both more than 0% of tree PFT cover (forest) and more than 0% of grass PFT cover (grassland). $T_c$ offsets ($\Delta T_{c,min}$, $\Delta T_{c,max}$ and $\Delta T_{c,mean}$) were generated using the same method although we note that unlike for $T_g$, the differentiation between forest and grassland $T_c$ also encapsulates a difference in height. In order to evaluate the effects of global warming on forest temperature offsets, we compared the historical climatology (1995–2014) to the future climatology (2080–2099). We focused our analysis on three major biomes (boreal, temperate and tropical) defined by the grid cells containing the corresponding tree PFTs (table A1). Geographically speaking, these categories roughly correspond to 3 latitudinal bands: between 50° and 70° for the boreal forest, 25° and 50° for the temperate forest and 0° and 25° for the tropical forest as shown on figure A1.

Finally, we constructed a global mean surface temperature anomaly relative to the 1850–1899 average ($\Delta GMT$), based on HadCrut5 observational data (Morice et al 2021). This allows to assess soil and canopy air temperature offsets per degree of global warming.

# 3. Results

## 3.1. Annual mean offset patterns

The annual average soil temperature offset (negative when the soil surface is cooler below the forest canopy compared to below the grassland, and positive otherwise) over the historical period features a pronounced latitudinal gradient, but with opposite signs for maximum and minimum soil temperature (figure 2). $\Delta T_{g,max}$ increases with latitude, from very negative values (–6 °C) in tropical regions to slightly positive values (+0.5 °C) in boreal regions indicating that tropical forests have a consistent buffering effect on forest understory maximum soil temperature whereas some boreal forests have a small amplifying effect. $\Delta T_{g,mean}$ shows a similar but smaller gradient (ranging between –2 and +0.5 °C) compared to $\Delta T_{g,max}$. $\Delta T_{c,min}$ was found to decrease with latitude, from slightly positive values (+1 °C) in the tropics to slightly negative values in the boreal region

(–2 °C), implying a buffering of minimum $T_g$ in tropical forests and an amplification in boreal forests. Overall, canopy air temperature offsets show similar latitudinal gradients during the historical period (figure E1), albeit with larger magnitudes, especially in boreal regions for which minimum $T_c$ and maximum $T_c$ are strongly amplified. However, $T_c$ and $T_g$ offsets differ in the tropics and subtropics where only maxima are dampened for $T_c$ (figures E1(g) and (i)) whereas both extremes are systematically buffered for $T_g$ (figures 2(g) and (i)).

Next, we compared the future period (2080–2099) forced by the high emission SSP5-8.5 scenario (~+4.4 K in global mean surface temperature, (IPCC 2021)), to the historical one (period 1850–1900). We calculated the difference between the temperature offset in the future relative to the historical baseline (figures 2(d)–(f)).

We found a significant negative difference for maximum $T_g$ in the tropics, suggesting that under high warming levels, tropical forests increase their buffering capacity for high temperature extremes by an average of 0.55 °C. This result is underlined by the shift in the distribution of tropical $\Delta T_{g,max}$ figure 2(g). In addition, a small shift of $\Delta T_{g,min}$ towards a zero-centered distribution in boreal forests implies less amplification of minimum soil temperatures during the future period.

Future changes for canopy air temperature offsets (figure E1) are qualitatively similar but with even larger amplitudes than for soil temperatures. In tropical forests, we observe that $\Delta T_{c,max}$ is buffered by 1.16 °C in the warmer period compared to the past period. We also found an increase of buffering capacities for boreal forests, decreasing the warming effect by ~0.43 °C (figure E1(g)). Finally, $\Delta T_{c,min}$ remains very small in the future over the tropical and temperate regions, but increases significantly at boreal latitudes (~0.34 °C change) while staying overall negative. Most boreal regions show a pronounced increase in temperature offset (over 3 °C on the Labrador peninsula), with forests further increasing minimum temperatures in the future, while Eastern Siberia shows negative values (figure E1(f)). This pattern reveals a contrasting biophysical response across the boreal region at higher warming levels with an overall reduced minimum $T_c$ amplification (the minimum temperature offset is less negative in the future) opposed to an increased minimum $T_c$ amplification over Siberia. However, this boreal dipole pattern is not visible on the $\Delta T_{g,min}$ future map (figure 2(f)), suggesting that it results from tree-atmosphere interactions rather than soil-tree interactions.

## 3.2. Seasonal offset patterns

Exploring the seasonality of temperature offsets, we find that annual patterns mask out a seasonal
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Global maps of simulated historical (1995–2014) soil temperature offset (forest–grassland; $\Delta T_g$) for maximum ($\Delta T_{g,max}$; a), mean ($\Delta T_{g,mean}$; b) and minimum ($\Delta T_{g,min}$; c) soil temperature. The difference between future (2080–2099) and historical (1995–2014) offset for maximum (d), mean (e) and minimum (f) soil temperature. Stippling marks areas where the difference is not statistically significant according to the Wilcoxon signed rank test (p-value > 0.05). $\Delta T_g$ distributions for historical (light blue) and future (dark blue) periods for maximum (g), mean (h) and minimum (i) soil temperature. Inner lines show distribution quartiles.</figcaption></figure>

complexity that is subject to change with global warming.

The tropical region displays a strong year-round buffering of maximum $T_g$ (negative $\Delta T_{g,max}$, figure 3) and, to a larger extent, of maximum $T_c$ (figure E2). $\Delta T_{g,min}$ is slightly positive (whereas $\Delta T_{c,min}$ is null) indicating a buffering of the highest and lowest soil temperatures. The negative difference between future and historical $\Delta T_{g,max}$ in the tropical region suggests a stronger yearly buffering of soil temperatures at the end of the century (figure 3(e)). A similar increase in future buffering capacity is observed for maximum canopy air temperature (figure E2(e)). In the boreal region, our simulations suggest a clear seasonal pattern of the $T_g$ offset (figure 3) even more pronounced for the $T_c$ offset (figure E2). $\Delta T_{g,min}$ is negative most of the year (boreal autumn, winter and spring) revealing an amplification process. $\Delta T_{g,max}$ is positive during boreal spring and summer, highlighting an amplification effect of forest on soil temperatures, while winter soil temperatures are slightly buffered. The amplitude of the canopy air temperature offsets (figure 3) are much larger than for soil temperature and feature a slightly different pattern. $\Delta T_{c,min}$ is also negative during boreal winter and spring and slightly positive the rest of the year. Conversely, $\Delta T_{c,max}$ is positive over the same period, extending until July for the northernmost latitudes, while it is moderately negative during the remaining months. $\Delta T_{c,max}$ is particularly latitude-dependent with peak values

reached later in the year for higher latitudes. These results suggest that there is a strong amplification of minimum and maximum boreal forest $T_c$ in winter and spring over the historical period.

The comparison with future $T_g$ offsets in the boreal region (figures 3(d) and (e)) indicates less $T_g$ amplification relative to the historical period, with increasing $\Delta T_{g,min}$ in winter and less positive $\Delta T_{g,max}$ in early spring. However, given the considerable spread both for $\Delta T_{g,min}$ and $\Delta T_{g,max}$ at high latitudes of the northern hemisphere, the signal in this region is not very robust (figure 3(f)). While $\Delta T_{g,max}$ is similar over both periods, $\Delta T_{g,min}$ is higher and much closer to zero during the future period, suggesting that there is little difference between soil temperatures inside forests compared to outside (figure 3(f)).

Figure 4 shows that the buffering of maximum $T_g$ and $T_c$ in tropical forests over the historical period further increases in the future global warming scenario (panel (f)). This figure also underlines the contrasted seasonal cycles for $T_g$ and $T_c$ offsets in temperate and boreal forests: (i) minimum $T_c$ is strongly amplified in winter and spring compared to minimum $T_g$, (ii) maximum $T_c$ is amplified whereas maximum $T_g$ is unchanged during spring in boreal forests and (iii) maximum $T_g$ is strongly buffered whereas maximum $T_c$ is slightly amplified during spring in temperate forests. Finally, in addition to future shifts in temperature offsets discussed in figure 3, we
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Global maps of simulated historical (1995–2014) soil temperature offset (forest–grassland; $\Delta T_g$) for maximum ($\Delta T_{g,max}$; a), mean ($\Delta T_{g,mean}$; b) and minimum ($\Delta T_{g,min}$; c) soil temperature. The difference between future (2080–2099) and historical (1995–2014) offset for maximum (d), mean (e) and minimum (f) soil temperature. Stippling marks areas where the difference is not statistically significant according to the Wilcoxon signed rank test (p-value > 0.05). $\Delta T_g$ distributions for historical (light blue) and future (dark blue) periods for maximum (g), mean (h) and minimum (i) soil temperature. Inner lines show distribution quartiles.</figcaption></figure>

complexity that is subject to change with global warming.

The tropical region displays a strong year-round buffering of maximum $T_g$ (negative $\Delta T_{g,max}$, figure 3) and, to a larger extent, of maximum $T_c$ (figure E2). $\Delta T_{g,min}$ is slightly positive (whereas $\Delta T_{c,min}$ is null) indicating a buffering of the highest and lowest soil temperatures. The negative difference between future and historical $\Delta T_{g,max}$ in the tropical region suggests a stronger yearly buffering of soil temperatures at the end of the century (figure 3(e)). A similar increase in future buffering capacity is observed for maximum canopy air temperature (figure E2(e)). In the boreal region, our simulations suggest a clear seasonal pattern of the $T_g$ offset (figure 3) even more pronounced for the $T_c$ offset (figure E2). $\Delta T_{g,min}$ is negative most of the year (boreal autumn, winter and spring) revealing an amplification process. $\Delta T_{g,max}$ is positive during boreal spring and summer, highlighting an amplification effect of forest on soil temperatures, while winter soil temperatures are slightly buffered. The amplitude of the canopy air temperature offsets (figure 3) are much larger than for soil temperature and feature a slightly different pattern. $\Delta T_{c,min}$ is also negative during boreal winter and spring and slightly positive the rest of the year. Conversely, $\Delta T_{c,max}$ is positive over the same period, extending until July for the northernmost latitudes, while it is moderately negative during the remaining months. $\Delta T_{c,max}$ is particularly latitude-dependent with peak values

reached later in the year for higher latitudes. These results suggest that there is a strong amplification of minimum and maximum boreal forest $T_c$ in winter and spring over the historical period.

The comparison with future $T_g$ offsets in the boreal region (figures 3(d) and (e)) indicates less $T_g$ amplification relative to the historical period, with increasing $\Delta T_{g,min}$ in winter and less positive $\Delta T_{g,max}$ in early spring. However, given the considerable spread both for $\Delta T_{g,min}$ and $\Delta T_{g,max}$ at high latitudes of the northern hemisphere, the signal in this region is not very robust (figure 3(f)). While $\Delta T_{g,max}$ is similar over both periods, $\Delta T_{g,min}$ is higher and much closer to zero during the future period, suggesting that there is little difference between soil temperatures inside forests compared to outside (figure 3(f)).

Figure 4 shows that the buffering of maximum $T_g$ and $T_c$ in tropical forests over the historical period further increases in the future global warming scenario (panel (f)). This figure also underlines the contrasted seasonal cycles for $T_g$ and $T_c$ offsets in temperate and boreal forests: (i) minimum $T_c$ is strongly amplified in winter and spring compared to minimum $T_g$, (ii) maximum $T_c$ is amplified whereas maximum $T_g$ is unchanged during spring in boreal forests and (iii) maximum $T_g$ is strongly buffered whereas maximum $T_c$ is slightly amplified during spring in temperate forests. Finally, in addition to future shifts in temperature offsets discussed in figure 3, we
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Soil temperature offsets as a function of time and latitude for historical minimum (a) and maximum (b) $T_g$ offsets and for the difference between future (2080–2099) and historical (1995–2014) offsets for minimum (d) and maximum (e) $T_g$ by means of a moving 11° latitude window. (c) and (f) Represent the corresponding annual zonal mean for the historical and for the future—historical offsets.</figcaption></figure>

observe a lag effect for future maximum temperature offsets as compared to the historical period, especially in boreal and temperature forests for both variables. In other words, the increase of maximum $T_g$ and $T_c$ buffering occurs earlier in the year under higher global warming.

### 3.3. Biome-dependent trends in temperature offsets per global warming level

Our transient simulation, spanning the 21st century, highlights biome-dependent offset trends per degree of warming (figure 5). The relationship between offsets and macroclimate is found to be linear across the century for all biomes. The long-term multi-year averages highlight a significant increase of maximum $T_g$ offset with global warming for all biomes, thereby confirming our hypothesis of stronger maximum temperature buffering in forest understories with increasing global temperatures. The magnitude of the maximum $T_g$ buffering increase is biome-dependent and particularly strong for tropical forests with an offset trend of -0.1 °C/°C. In contrast, minimum $T_g$ offset trends vary across biomes with negative values for tropical forests and positive values

for temperate and boreal forests. Similar results are observed for maximum $T_c$ offset. The maximum $T_c$ offset trend is particularly strong in tropical forests with -0.2 °C/°C (figure E3).

## 4. Discussion

### 4.1. Contrasting seasonal and latitudinal effects of forests on temperature offsets

In this study, we provide a first process-based modeling assessment of global forest effects on microclimate under a warmer climate, complementing observation-based approaches (Haesen et al 2021, De Lombaerde et al 2022, Lembrechts et al 2022). For historical annual averages (figures 2 and E1), we find a positive latitudinal gradient for mean and maximum $T_g$ and $T_c$ offsets with a strong forest buffering effect in the tropics, consistent with observations by De Lombaerde et al (2022) and Lembrechts et al (2022). In the boreal region, the simulated $\Delta T_{g,max}$ matches the results of De Lombaerde et al (2022), with near-zero values, while our simulated $\Delta T_{c,max}$ indicates a strong amplification in the canopy. The main difference between our results and empirical
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Soil temperature offsets as a function of time and latitude for historical minimum (a) and maximum (b) *T*<sub>g</sub> offsets and for the difference between future (2080–2099) and historical (1995–2014) offsets for minimum (d) and maximum (e) *T*<sub>g</sub> by means of a moving 11° latitude window. (c) and (f) Represent the corresponding annual zonal mean for the historical and for the future—historical offsets.</figcaption></figure>

observe a lag effect for future maximum temperature
offsets as compared to the historical period, especially
in boreal and temperature forests for both variables.
In other words, the increase of maximum Tg and
Tc buffering occurs earlier in the year under higher
global warming.

**3.3. Biome-dependent trends in temperature offsets per global warming level**

Our transient simulation, spanning the 21st century,
highlights biome-dependant offset trends per degree
of warming (figure 5). The relationship between off-
sets and macroclimate is found to be linear across
the century for all biomes. The long-term multi-
year averages highlight a significant increase of max-
imum T<sub>g</sub> offset with global warming for all biomes,
thereby confirming our hypothesis of stronger max-
imum temperature buffering in forest understories
with increasing global temperatures. The magnitude
of the maximum T<sub>g</sub> buffering increase is biome-
dependent and particularly strong for tropical forests
with an offset trend of -0.1 °C/°C. In contrast, min-
imum T<sub>g</sub> offset trends vary across biomes with neg-
ative values for tropical forests and positive values

for temperate and boreal forests. Similar results are
observed for maximum T<sub>c</sub> offset. The maximum T<sub>c</sub>
offset trend is particularly strong in tropical forests
with -0.2 °C/°C (figure E3).

4. Discussion

4.1. Contrasting seasonal and latitudinal effects of
forests on temperature offsets

In this study, we provide a first process-based mod-
eling assessment of global forest effects on micro-
climate under a warmer climate, complementing
observation-based approaches (Haesen et al 2021, De
Lombaerde et al 2022, Lembrechts et al 2022). For
historical annual averages (figures 2 and E1), we find
a positive latitudinal gradient for mean and max-
imum T<sub>g</sub> and T<sub>c</sub> offsets with a strong forest buffer-
ing effect in the tropics, consistent with observations
by De Lombaerde et al (2022) and Lembrechts et al
(2022). In the boreal region, the simulated ΔT<sub>g,max</sub>
matches the results of De Lombaerde et al (2022),
with near-zero values, while our simulated ΔT<sub>c,max</sub>
indicates a strong amplification in the canopy. The
main difference between our results and empirical
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Seasonal cycle of historical (1995–2014) and future (2080–2099) temperature offsets for the globe (a) and (b) and averaged over the boreal (c) and (d), temperate (e) and (f) and tropical (g) and (h) forest biomes (defined on figure A1). Minimum temperatures (first column) and maximum temperatures (second column) include canopy air temperature (*T*<sub>c</sub>) and soil temperature (*T*<sub>g</sub>). The envelope represents the standard deviation. Note the different y-axis scales.</figcaption></figure>

findings is observed for the minimum temperature
offset: De Lombaerde *et al* (2022) show buffered min-
imum temperatures in boreal forests whereas we find
that minimum *T*<sub>c</sub> and *T*<sub>g</sub> are amplified compared to
grassland. The strong amplification of minimum can-
opy temperature at high latitudes can be explained

by multiple effects. First, the presence of snow over
grassland canopy may buffer grassland T_c compared
to forest T_c. Additionally, CLM5.1 is known to have
5 to 10 m taller canopies than observed for the
boreal needleleaf evergreen PFT (Lawrence et al 2019)
which accounts for the majority of tree PFTs at high
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Seasonal cycle of historical (1995–2014) and future (2080–2099) temperature offsets for the globe (a) and (b) and averaged over the boreal (c) and (d), temperate (e) and (f) and tropical (g) and (h) forest biomes (defined on figure A1). Minimum temperatures (first column) and maximum temperatures (second column) include canopy air temperature ($T_c$) and soil temperature ($T_g$). The envelope represents the standard deviation. Note the different y-axis scales.</figcaption></figure>

findings is observed for the minimum temperature offset: De Lombaerde et al. (2022) show buffered minimum temperatures in boreal forests whereas we find that minimum $T_c$ and $T_g$ are amplified compared to grassland. The strong amplification of minimum canopy temperature at high latitudes can be explained

by multiple effects. First, the presence of snow over grassland canopy may buffer grassland $T_c$ compared to forest $T_c$. Additionally, CLM5.1 is known to have 5 to 10 m taller canopies than observed for the boreal needleleaf evergreen PFT (Lawrence et al. 2019) which accounts for the majority of tree PFTs at high
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Changes in soil temperature offset per degree of global mean warming for (a) minimum $T_g$, (b) mean $T_g$ and (c) maximum $T_g$. The global mean surface temperature anomaly ΔGMT is given relative to the 1850–1899 average using HadCRUT5 data. Δ$T_g$ and ΔGMT signals are both 20 year rolling averages over the 1995–2099 period. s (°C/°C) is the linear regression slope of the temperature offset per degree of global warming given for each forest biome. The offset values per global warming degrees are shown in appendix table D1.</figcaption></figure>

latitudes. Higher canopies generate stronger turbulence that decreases canopy temperature over these forested areas.

The simulated latitudinal gradients are the result of strong seasonal signals. Given the low incoming solar radiation during winter in boreal regions, variations in surface albedo between plant types have a negligible impact on $T_g$. Therefore, it is more likely that the simulated amplification of minimum $T_g$ is caused by differences in surface roughness. The roughness length for tree PFTs is larger compared to grassland, and this is especially the case for snow-covered grassland (Meier et al 2021). Hence, over the forest canopy there is more turbulent mixing and therefore higher sensible heat fluxes compared to grassland, which decreases forest soil temperature. Given our definition of the canopy temperature offset, the negative wintertime $\Delta T_{c,min}$ at high latitudes significantly differs from seasonal observations by Lembrechts et al (2022) for which it is positive.

As daytime lengthens through spring, the surface albedo difference between forested and grassland areas increases in importance in the radiative budget. Qu and Hall (2014) have shown that there is a strong correlation between the surface albedo feedback and local surface warming, and that the largest correlations are located at increasing latitudes across springtime. Interestingly, the maximum $T_c$ offset signal (figure E1(b)) follows such a pattern as spring progresses into summer, suggesting that the surface albedo feedback could be an important driver of forest canopy air temperature amplification in the boreal region. Several other studies (Lee et al 2011, Duveiller et al 2018) focusing on surface temperature also support a local forest-induced warming at

boreal latitudes. Given these complex effects at high latitude, future studies should pay attention to these PFT-dependant snow effects on canopy temperature. In particular, the effect of snow on grassland canopy could undermine the use of $T_c$ for analyzing buffering effects in colder regions. We note here that the interactions between snow and vegetation are also held responsible for the low predictive quality of microclimate temperatures in Lembrechts et al (2022).

The simulated future increase of maximum $T_g$ and $T_c$ offsets in tropical forests corresponds to the expected response under global warming, assuming a space-for-time substitution and no constraint on water availability (De Frenne et al 2021). The simulated maximum $T_g$ offset trend of -0.1 °C/°C is consistent with De Lombaerde et al (2022) but the values (ranging from ~ -3.5 °C to ~ -4 °C across the century (appendix table D1)) indicate less buffering than what they observe (~ -5 °C to ~ -5.5 °C). The observed linear relationship between forest microclimate and global mean temperature (figure 5) is linked to the model assumption of static forests in terms of distribution and surface. Finally, the seasonal shifts in future temperature offsets for temperate and boreal biomes could be attributed to the simulated snow dynamics featuring a shorter snow cover duration and a smaller fraction of snow-covered ground in the future (figure 4). We expect that simulating phenological responses to the changing climate could generate an extra lag effect on the temperature offset.

## 4.2. Potential impacts of maximum temperature offset trends on tropical biodiversity

Our results show that future maximum $T_g$ offset change and the associated buffering is stronger in
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Changes in soil temperature offset per degree of global mean warming for (a) minimum $T_g$, (b) mean $T_g$ and (c) maximum $T_g$. The global mean surface temperature anomaly ΔGMT is given relative to the 1850–1899 average using HadCRUT5 data. Δ$T_g$ and ΔGMT signals are both 20 year rolling averages over the 1995–2099 period. s (°C/°C) is the linear regression slope of the temperature offset per degree of global warming given for each forest biome. The offset values per global warming degrees are shown in appendix table D1.</figcaption></figure>

latitudes. Higher canopies generate stronger turbulence that decreases canopy temperature over these forested areas.

The simulated latitudinal gradients are the result of strong seasonal signals. Given the low incoming solar radiation during winter in boreal regions, variations in surface albedo between plant types have a negligible impact on $T_g$. Therefore, it is more likely that the simulated amplification of minimum $T_g$ is caused by differences in surface roughness. The roughness length for tree PFTs is larger compared to grassland, and this is especially the case for snow-covered grassland (Meier et al 2021). Hence, over the forest canopy there is more turbulent mixing and therefore higher sensible heat fluxes compared to grassland, which decreases forest soil temperature. Given our definition of the canopy temperature offset, the negative wintertime $\Delta T_{c,min}$ at high latitudes significantly differs from seasonal observations by Lembrechts et al (2022) for which it is positive.

As daytime lengthens through spring, the surface albedo difference between forested and grassland areas increases in importance in the radiative budget. Qu and Hall (2014) have shown that there is a strong correlation between the surface albedo feedback and local surface warming, and that the largest correlations are located at increasing latitudes across springtime. Interestingly, the maximum $T_c$ offset signal (figure E1(b)) follows such a pattern as spring progresses into summer, suggesting that the surface albedo feedback could be an important driver of forest canopy air temperature amplification in the boreal region. Several other studies (Lee et al 2011, Duveiller et al 2018) focusing on surface temperature also support a local forest-induced warming at

boreal latitudes. Given these complex effects at high latitude, future studies should pay attention to these PFT-dependant snow effects on canopy temperature. In particular, the effect of snow on grassland canopy could undermine the use of $T_c$ for analyzing buffering effects in colder regions. We note here that the interactions between snow and vegetation are also held responsible for the low predictive quality of microclimate temperatures in Lembrechts et al (2022).

The simulated future increase of maximum $T_g$ and $T_c$ offsets in tropical forests corresponds to the expected response under global warming, assuming a space-for-time substitution and no constraint on water availability (De Frenne et al 2021). The simulated maximum $T_g$ offset trend of -0.1 °C/°C is consistent with De Lombaerde et al (2022) but the values (ranging from ~ -3.5 °C to ~ -4 °C across the century (appendix table D1)) indicate less buffering than what they observe (~ -5 °C to ~ -5.5 °C). The observed linear relationship between forest microclimate and global mean temperature (figure 5) is linked to the model assumption of static forests in terms of distribution and surface. Finally, the seasonal shifts in future temperature offsets for temperate and boreal biomes could be attributed to the simulated snow dynamics featuring a shorter snow cover duration and a smaller fraction of snow-covered ground in the future (figure 4). We expect that simulating phenological responses to the changing climate could generate an extra lag effect on the temperature offset.

## 4.2. Potential impacts of maximum temperature offset trends on tropical biodiversity

Our results show that future maximum $T_g$ offset change and the associated buffering is stronger in
tropical forests than for higher latitudes (figure 5). Given such a latitude-dependant trend, we identify two important implications for tropical biodiversity. First, tropical forests could become a key refugia for thermosensitive understory species relative to other forested biomes in a warmer world. Second, such species might accumulate an even higher climatic debt than their temperate forest counterparts for which microclimate buffering already explains part of species' persistence rather than migration (Bertrand et al 2016). Given that tropical species have narrower thermal niches than temperate ones (Deutsch et al 2008, Tewksbury et al 2008, Jirinec et al 2022), we expect that they may fare worse if tropical forests were to lose their buffering effect. In fact, despite continuous canopy buffering, Trew et al (2023) shows that understory microclimate has already undergone significant changes over the 1990–2019 period. Additionally, tropical canopies are currently approaching their critical temperature threshold and will most likely exceed it within RCP 8.5 (Doughty et al 2023), causing tree death and subsequent temperature buffering failure. The maximum Tg offset trends for boreal and temperate forests are relatively smaller than for tropical forests and the implications of such temperature changes on biodiversity would require more in depth research.

### 4.3. Perspectives and direction for modeling future forest microclimates

Forest microclimates are at the core of a large set of interdisciplinary research questions, including species' response to global changes, forest regeneration dynamics or adaptation strategies to preserve biodiversity (De Frenne et al 2021, Kemppinen et al 2023). Land surface models such as CLM5.1, linking atmosphere dynamics to land-based ecological processes within Earth System Models, are crucial tools to address these complex questions. Nevertheless, our study is subject to limitations, and addressing these through model development would improve the representation of microclimates in forests. The first important limitation here is the assumption that forests are static, i.e. their composition and structure have no temporal dynamics aside from seasonal fluctuations in LAI. This assumption allows to isolate and focus on the biophysical response of present-day forest cover in future climates, without considering additional uncertainties related to forest dynamics under a changing climate and the choice of land-use scenarios. In particular, the responses of LAI seasonality and phenological dates, which strongly modulate the surface energy balance, to global temperature increase is not covered by the SP mode (Park and Jeong 2021). How would our results change with an evolving phenology and physiology? This question is

out of the scope of this study and would require the use of a simulation with prognostic carbon pools, which would provide an additional layer of complexity by representing climate vegetation feedbacks, and make the simulations subject to existing biases in the processes simulating LAI, which can be substantial. In these circumstances, we could either expect less temperature buffering due to lower LAI or more temperature buffering induced by longer growing seasons and more CO2-induced LAI.

Additionally, the present analysis limits describing temperature offset to temperature extremes, hereby omitting humidity extremes, which are acknowledged to have a considerable influence on forest biodiversity (Aguirre-Gutiérrez et al 2020). Studying the effects of droughts and the combination of humidity and temperature extremes are important avenues future research could explore.

The other limitations are inherent in the structure of CLM5.1 model itself. For instance, there is no horizontal transport of energy between tiles in the land model, the energy budget being solved for the vertical land-atmosphere interactions only. Although the new roughness parametrization used here allows for a more accurate vertical energy budget, the single layer canopy representation of forests in CLM5.1 does not consider vertical heterogeneity within the canopy of a given PFT (Bonan et al 2021). We describe some candidate future modeling pathway in appendix C.1. Finally, the 0.5° by 0.5° resolution used here (despite being high for LSM simulations) is too coarse to account for local structural heterogeneity of forest stands such as canopy gaps, vegetation density, proximity to rivers and topography known to be drivers of forest microclimates (Bramer et al 2018, De Frenne et al 2021, Malle et al 2021).

## 5. Conclusion

This study assesses the effect of global warming on forest understory temperatures in CLM5.1. We find a strong buffering of maximum soil and canopy air temperatures in tropical forests. This buffering effect increases with global warming suggesting that tropical species could accumulate a larger climatic debt than at higher latitudes, provided that trees remain alive. On the contrary, boreal forests slightly amplify maximum canopy air temperature compared to grassland areas. In boreal forests, our results also highlight a strong seasonal cycle with a large temperature amplification during boreal winter calling for more seasonal-based studies focusing on snow cover effects on microclimate. We encourage future LSM developments to include and assess additional microclimate drivers such as canopy structure, forest fragmentation or forest regeneration, which were out
tropical forests than for higher latitudes (figure 5). Given such a latitude-dependant trend, we identify two important implications for tropical biodiversity. First, tropical forests could become a key refugia for thermosensitive understory species relative to other forested biomes in a warmer world. Second, such species might accumulate an even higher climatic debt than their temperate forest counterparts for which microclimate buffering already explains part of species' persistence rather than migration (Bertrand et al 2016). Given that tropical species have narrower thermal niches than temperate ones (Deutsch et al 2008, Tewksbury et al 2008, Jirinec et al 2022), we expect that they may fare worse if tropical forests were to lose their buffering effect. In fact, despite continuous canopy buffering, Trew et al (2023) shows that understory microclimate has already undergone significant changes over the 1990–2019 period. Additionally, tropical canopies are currently approaching their critical temperature threshold and will most likely exceed it within RCP 8.5 (Doughty et al 2023), causing tree death and subsequent temperature buffering failure. The maximum Tg offset trends for boreal and temperate forests are relatively smaller than for tropical forests and the implications of such temperature changes on biodiversity would require more in depth research.

### 4.3. Perspectives and direction for modeling future forest microclimates

Forest microclimates are at the core of a large set of interdisciplinary research questions, including species' response to global changes, forest regeneration dynamics or adaptation strategies to preserve biodiversity (De Frenne et al 2021, Kemppinen et al 2023). Land surface models such as CLM5.1, linking atmosphere dynamics to land-based ecological processes within Earth System Models, are crucial tools to address these complex questions. Nevertheless, our study is subject to limitations, and addressing these through model development would improve the representation of microclimates in forests. The first important limitation here is the assumption that forests are static, i.e. their composition and structure have no temporal dynamics aside from seasonal fluctuations in LAI. This assumption allows to isolate and focus on the biophysical response of present-day forest cover in future climates, without considering additional uncertainties related to forest dynamics under a changing climate and the choice of land-use scenarios. In particular, the responses of LAI seasonality and phenological dates, which strongly modulate the surface energy balance, to global temperature increase is not covered by the SP mode (Park and Jeong 2021). How would our results change with an evolving phenology and physiology? This question is

out of the scope of this study and would require the use of a simulation with prognostic carbon pools, which would provide an additional layer of complexity by representing climate vegetation feedbacks, and make the simulations subject to existing biases in the processes simulating LAI, which can be substantial. In these circumstances, we could either expect less temperature buffering due to lower LAI or more temperature buffering induced by longer growing seasons and more CO2-induced LAI.

Additionally, the present analysis limits describing temperature offset to temperature extremes, hereby omitting humidity extremes, which are acknowledged to have a considerable influence on forest biodiversity (Aguirre-Gutiérrez et al 2020). Studying the effects of droughts and the combination of humidity and temperature extremes are important avenues future research could explore.

The other limitations are inherent in the structure of CLM5.1 model itself. For instance, there is no horizontal transport of energy between tiles in the land model, the energy budget being solved for the vertical land-atmosphere interactions only. Although the new roughness parametrization used here allows for a more accurate vertical energy budget, the single layer canopy representation of forests in CLM5.1 does not consider vertical heterogeneity within the canopy of a given PFT (Bonan et al 2021). We describe some candidate future modeling pathway in appendix C.1. Finally, the 0.5° by 0.5° resolution used here (despite being high for LSM simulations) is too coarse to account for local structural heterogeneity of forest stands such as canopy gaps, vegetation density, proximity to rivers and topography known to be drivers of forest microclimates (Bramer et al 2018, De Frenne et al 2021, Malle et al 2021).

## 5. Conclusion

This study assesses the effect of global warming on forest understory temperatures in CLM5.1. We find a strong buffering of maximum soil and canopy air temperatures in tropical forests. This buffering effect increases with global warming suggesting that tropical species could accumulate a larger climatic debt than at higher latitudes, provided that trees remain alive. On the contrary, boreal forests slightly amplify maximum canopy air temperature compared to grassland areas. In boreal forests, our results also highlight a strong seasonal cycle with a large temperature amplification during boreal winter calling for more seasonal-based studies focusing on snow cover effects on microclimate. We encourage future LSM developments to include and assess additional microclimate drivers such as canopy structure, forest fragmentation or forest regeneration, which were out
of the scope of this study. To this end, the eco-
logy, biogeography and climate science communit-
ies should determine collectively the most promising
approaches for forest microclimate modeling and for
use in evidence-based decision-making.

Data availability statement

The data that support the findings of this study are
openly available at the following URL/DOI: https://
doi.org/10.5281/zenodo.8308545.

Acknowledgments

We are very grateful to Jonathan Buzan for his assist-
ance in running CLM5.1, to Marie-Estelle Demory
for supporting simulation processing and to Emilie
Joetzjer for thoughtful suggestions on this manu-
script. We thank Keith Oleson, William J Sacks,

Jim Edwards and Petra Sieber for their support
on the DiscussCESM forum and the CESM com-
munity for making CLM5.1 documentation avail-
able. Finally, we acknowledge Eva Gril and Jonathan
Lenoir for seminal discussions on forest microcli-
mates. Computational resources were provided by
the Swiss national supercomputing center (CSCS).
R F acknowledges funding by the European Union's
Horizon 2020 (H2020) research and innovation
program under Grant Agreement No. 101003536
(ESM2025—Earth System Models for the Future)
and 821003 (4 C, Climate-Carbon Interactions in the
Coming Century). J O and J C acknowledge fund-
ing by the French ‘Agence Nationale de la Recherche’
(ANR) under grant ANR-21-CE32-0012 (MaCCMic
project). Assuming model simulations to be the
largest emission source, the estimated carbon foot-
print of this study (following Lannelongue et al
(2021)) is 600 kgCO₂.
of the scope of this study. To this end, the eco-
logy, biogeography and climate science communit-
ies should determine collectively the most promising
approaches for forest microclimate modeling and for
use in evidence-based decision-making.

Data availability statement

The data that support the findings of this study are
openly available at the following URL/DOI: https://
doi.org/10.5281/zenodo.8308545.

Acknowledgments

We are very grateful to Jonathan Buzan for his assist-
ance in running CLM5.1, to Marie-Estelle Demory
for supporting simulation processing and to Emilie
Joetzjer for thoughtful suggestions on this manu-
script. We thank Keith Oleson, William J Sacks,

Jim Edwards and Petra Sieber for their support
on the DiscussCESM forum and the CESM com-
munity for making CLM5.1 documentation avail-
able. Finally, we acknowledge Eva Gril and Jonathan
Lenoir for seminal discussions on forest microcli-
mates. Computational resources were provided by
the Swiss national supercomputing center (CSCS).
R F acknowledges funding by the European Union's
Horizon 2020 (H2020) research and innovation
program under Grant Agreement No. 101003536
(ESM2025—Earth System Models for the Future)
and 821003 (4 C, Climate-Carbon Interactions in the
Coming Century). J O and J C acknowledge fund-
ing by the French ‘Agence Nationale de la Recherche’
(ANR) under grant ANR-21-CE32-0012 (MaCCMic
project). Assuming model simulations to be the
largest emission source, the estimated carbon foot-
print of this study (following Lannelongue et al
(2021)) is 600 kgCO₂.
## Appendix A. PFT based biomes

<table><caption>**Table A1.** PFT categorization of forest and grassland environments based on Lawrence *et al* (2020) documentation.</caption><thead><tr><th>Environment</th><th>PFT</th><th>PFT index</th></tr></thead><tbody><tr><td rowspan="8">Forest</td><td>Needleleaf evergreen tree—temperate</td><td>1</td></tr><tr><td>Needleleaf evergreen tree—boreal</td><td>2</td></tr><tr><td>Needleleaf deciduous tree—boreal</td><td>3</td></tr><tr><td>Broadleaf evergreen tree—tropical</td><td>4</td></tr><tr><td>Broadleaf evergreen tree—temperate</td><td>5</td></tr><tr><td>Broadleaf deciduous tree—tropical</td><td>6</td></tr><tr><td>Broadleaf deciduous tree—temperate</td><td>7</td></tr><tr><td>Broadleaf deciduous tree—boreal</td><td>8</td></tr><tr><td rowspan="3">Grassland</td><td>C<sub>3</sub> arctic grass</td><td>12</td></tr><tr><td>C<sub>3</sub> grass</td><td>13</td></tr><tr><td>C<sub>4</sub> grass</td><td>14</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Figure A1.** Map of PFT-based forest biomes used in this study. The boreal biome (red) is made of PFT indices 2, 3 and 8, the temperate biome (green) corresponds to PFT indices 1, 5 and 7 and the tropical biome (blue) encompasses PFT indices 4 and 6 (see table A1).</figcaption></figure>

## Appendix B. Model simulation experiment design

<figure><img src="image_11.png" /><figcaption>**Figure B1.** Model simulation experiment design. The spin-up phase allows CLM5.1 to reach a state of statistical equilibrium under the applied forcing. Following this, two land-only simulations are run using two different settings. The historical period follows the HIST_DATM%GSWP3v1_CLM51%SP_SICE_SOCN_MOSART_CISM2%NOEVOLVE_SWAV 'compset' (in CESM specific notation) applying the GSWPv3 reanalysis forcing. The future simulation follows the SSP585_DATM%GSWP3v1_CLM51%SP_SICE_SOCN_MOSART_SGLC_SWAV 'compset' integrating future SSP5-8.5 climate anomalies.</figcaption></figure>
## Appendix A. PFT based biomes

<table><caption>**Table A1.** PFT categorization of forest and grassland environments based on Lawrence *et al* (2020) documentation.</caption><thead><tr><th>Environment</th><th>PFT</th><th>PFT index</th></tr></thead><tbody><tr><td rowspan="8">Forest</td><td>Needleleaf evergreen tree—temperate</td><td>1</td></tr><tr><td>Needleleaf evergreen tree—boreal</td><td>2</td></tr><tr><td>Needleleaf deciduous tree—boreal</td><td>3</td></tr><tr><td>Broadleaf evergreen tree—tropical</td><td>4</td></tr><tr><td>Broadleaf evergreen tree—temperate</td><td>5</td></tr><tr><td>Broadleaf deciduous tree—tropical</td><td>6</td></tr><tr><td>Broadleaf deciduous tree—temperate</td><td>7</td></tr><tr><td>Broadleaf deciduous tree—boreal</td><td>8</td></tr><tr><td rowspan="3">Grassland</td><td>C<sub>3</sub> arctic grass</td><td>12</td></tr><tr><td>C<sub>3</sub> grass</td><td>13</td></tr><tr><td>C<sub>4</sub> grass</td><td>14</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Figure A1.** Map of PFT-based forest biomes used in this study. The boreal biome (red) is made of PFT indices 2, 3 and 8, the temperate biome (green) corresponds to PFT indices 1, 5 and 7 and the tropical biome (blue) encompasses PFT indices 4 and 6 (see table A1).</figcaption></figure>

## Appendix B. Model simulation experiment design

<figure><img src="image_11.png" /><figcaption>**Figure B1.** Model simulation experiment design. The spin-up phase allows CLM5.1 to reach a state of statistical equilibrium under the applied forcing. Following this, two land-only simulations are run using two different settings. The historical period follows the HIST_DATM%GSWP3v1_CLM51%SP_SICE_SOCN_MOSART_CISM2%NOEVOLVE_SWAV 'compset' (in CESM specific notation) applying the GSWPv3 reanalysis forcing. The future simulation follows the SSP585_DATM%GSWP3v1_CLM51%SP_SICE_SOCN_MOSART_SGLC_SWAV 'compset' integrating future SSP5-8.5 climate anomalies.</figcaption></figure>
# Appendix C. Reflections on canopy representation

## C.1. Canopy air temperature in CLM5.1

In Clm5.1, $T_c$ ('TAF' in CESM specific notation) is defined as a weighted mean of the atmospheric potential temperature ($\Theta_{atm}$, °C), the vegetation temperature ($T_v$, °C) and the soil temperature ($T_g$, °C):

$$T_c = \frac{c_a^h \Theta_{atm} + c_g^h T_g + c_v^h T_v}{c_a^h + c_g^h + c_v^h} \quad (C1)$$

where $c_a^h$, $c_g^h$ and $c_v^h$ are the sensible heat conductances from the canopy air to the atmosphere, from the ground to the canopy air, and from the leaf surface to the canopy air, respectively ($ms^{-1}$). $\Theta_{atm}$ is provided by the atmospheric temperature forcing TBOT at height $z_{bot}$ following $\Theta_{atm} = \text{TBOT} + \Gamma_d z_{bot}$ where $\Gamma_d = 0.0098 \text{ Km}^{-1}$ is the negative of the dry adiabatic lapse rate. $T_v$ represents the leaf temperature derived from the total sensible heat flux balance.

## C.2. Potential pathways for improved canopy representations

Here we propose model development pathways to enhance the representation of forest microclimate processes. First, adopting a multilayer canopy representation was proven to improve the radiative balance across the canopy in CLM for point-based sites (Bonan et al 2021). Incorporating such vertical canopy descriptions at global scales could help to diagnose the relative role of roughness and hydrology processes for different forest biomes. On the

other hand, rerunning the present simulation with a dynamical vegetation would allow to question the evolution of forest microclimates considering ecological shifts, regeneration dynamics for different land-use scenarios and potential forest tipping points. CLM-FATES model is well suited for such experiments because it adds a size- and age-structured representation of vegetation dynamics within the land surface model structure (Fisher et al 2018). In particular, the integration of a multilayer scheme in CLM-FATES would allow finer evaluation of age- and species-related effects on forest microclimates at multiple storey heights. Alternatively, building an under-story air temperature variable as a weighted mean of $T_g$ and $T_c$ could prove a promising path for future CLM developments, provided that such a weighted mean can be easily constructed for all forest biomes and atmospheric stability condition. Ultimately, modeling forest microclimates requires a variety of spatial scales in order to account for the different driving processes. For instance, investigating the effect of forest fragmentation on forest microclimate would require a meter-scale resolution and the implementation of a distance metric to represent proximity to forest edges. Determining the level of complexity (including the number of processes and the degree of spatial resolution) needed to represent forest microclimate dynamics would be very valuable in order to optimize the balance between realism and energy consumption at the heart of our modeling issues. It might be that regional models are more appropriate than global models to provide forest microclimate projections.
# Appendix C. Reflections on canopy representation

## C.1. Canopy air temperature in CLM5.1

In Clm5.1, $T_c$ ('TAF' in CESM specific notation) is defined as a weighted mean of the atmospheric potential temperature ($\Theta_{atm}$, °C), the vegetation temperature ($T_v$, °C) and the soil temperature ($T_g$, °C):

$$T_c = \frac{c_a^h \Theta_{atm} + c_g^h T_g + c_v^h T_v}{c_a^h + c_g^h + c_v^h} \quad (C1)$$

where $c_a^h$, $c_g^h$ and $c_v^h$ are the sensible heat conductances from the canopy air to the atmosphere, from the ground to the canopy air, and from the leaf surface to the canopy air, respectively ($ms^{-1}$). $\Theta_{atm}$ is provided by the atmospheric temperature forcing TBOT at height $z_{bot}$ following $\Theta_{atm} = \text{TBOT} + \Gamma_d z_{bot}$ where $\Gamma_d = 0.0098 \text{ Km}^{-1}$ is the negative of the dry adiabatic lapse rate. $T_v$ represents the leaf temperature derived from the total sensible heat flux balance.

## C.2. Potential pathways for improved canopy representations

Here we propose model development pathways to enhance the representation of forest microclimate processes. First, adopting a multilayer canopy representation was proven to improve the radiative balance across the canopy in CLM for point-based sites (Bonan et al 2021). Incorporating such vertical canopy descriptions at global scales could help to diagnose the relative role of roughness and hydrology processes for different forest biomes. On the

other hand, rerunning the present simulation with a dynamical vegetation would allow to question the evolution of forest microclimates considering ecological shifts, regeneration dynamics for different land-use scenarios and potential forest tipping points. CLM-FATES model is well suited for such experiments because it adds a size- and age-structured representation of vegetation dynamics within the land surface model structure (Fisher et al 2018). In particular, the integration of a multilayer scheme in CLM-FATES would allow finer evaluation of age- and species-related effects on forest microclimates at multiple storey heights. Alternatively, building an under-story air temperature variable as a weighted mean of $T_g$ and $T_c$ could prove a promising path for future CLM developments, provided that such a weighted mean can be easily constructed for all forest biomes and atmospheric stability condition. Ultimately, modeling forest microclimates requires a variety of spatial scales in order to account for the different driving processes. For instance, investigating the effect of forest fragmentation on forest microclimate would require a meter-scale resolution and the implementation of a distance metric to represent proximity to forest edges. Determining the level of complexity (including the number of processes and the degree of spatial resolution) needed to represent forest microclimate dynamics would be very valuable in order to optimize the balance between realism and energy consumption at the heart of our modeling issues. It might be that regional models are more appropriate than global models to provide forest microclimate projections.
## Appendix D. Future temperature offsets per degree of global warming

<table><caption>**Table D1.** Soil temperature minimum, mean and maximum offsets (°C) per degree of global mean warming ΔGMT (°C). Standard deviation is given in brackets.</caption><thead><tr><th>ΔGMT</th><th></th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td rowspan="3">Boreal</td><td>ΔT<sub>g,min</sub></td><td>-0.91 [0.94]</td><td>-0.89 [0.92]</td><td>-0.83 [0.91]</td><td>-0.78 [0.89]</td><td>-0.73 [0.89]</td><td>-0.67 [0.89]</td></tr><tr><td>ΔT<sub>g,mean</sub></td><td>-0.57 [0.84]</td><td>-0.57 [0.83]</td><td>-0.54 [0.82]</td><td>-0.53 [0.82]</td><td>-0.51 [0.82]</td><td>-0.49 [0.83]</td></tr><tr><td>ΔT<sub>g,max</sub></td><td>-0.66 [1.12]</td><td>-0.68 [1.12]</td><td>-0.7 [1.13]</td><td>-0.72 [1.13]</td><td>-0.74 [1.13]</td><td>-0.76 [1.14]</td></tr><tr><td rowspan="3">Temperate</td><td>ΔT<sub>g,min</sub></td><td>-0.33 [0.76]</td><td>-0.3 [0.74]</td><td>-0.27 [0.71]</td><td>-0.24 [0.69]</td><td>-0.21 [0.67]</td><td>-0.18 [0.64]</td></tr><tr><td>ΔT<sub>g,mean</sub></td><td>-0.65 [0.5]</td><td>-0.66 [0.5]</td><td>-0.67 [0.51]</td><td>-0.67 [0.52]</td><td>-0.68 [0.52]</td><td>-0.69 [0.53]</td></tr><tr><td>ΔT<sub>g,max</sub></td><td>-1.65 [1.13]</td><td>-1.7 [1.15]</td><td>-1.75 [1.16]</td><td>-1.8 [1.16]</td><td>-1.85 [1.17]</td><td>-1.9 [1.17]</td></tr><tr><td rowspan="3">Tropical</td><td>ΔT<sub>g,min</sub></td><td>0.52 [0.52]</td><td>0.49 [0.5]</td><td>0.44 [0.49]</td><td>0.4 [0.47]</td><td>0.36 [0.46]</td><td>0.32 [0.46]</td></tr><tr><td>ΔT<sub>g,mean</sub></td><td>-0.9 [0.51]</td><td>-0.94 [0.51]</td><td>-0.99 [0.51]</td><td>-1.03 [0.51]</td><td>-1.08 [0.51]</td><td>-1.12 [0.51]</td></tr><tr><td>ΔT<sub>g,max</sub></td><td>-3.47 [1.38]</td><td>-3.57 [1.4]</td><td>-3.67 [1.42]</td><td>-3.77 [1.44]</td><td>-3.87 [1.47]</td><td>-3.96 [1.48]</td></tr></tbody></table>

<table><caption>**Table D2.** Canopy air temperature minimum, mean and maximum offsets (°C) per degree of global mean warming ΔGMT (°C). Standard deviation is given in brackets.</caption><thead><tr><th>ΔGMT</th><th></th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td rowspan="3">Boreal</td><td>ΔT<sub>c,min</sub></td><td>-2.06 [1.98]</td><td>-2.08 [1.94]</td><td>-2.02 [1.84]</td><td>-1.92 [1.74]</td><td>-1.85 [1.65]</td><td>-1.8 [1.61]</td></tr><tr><td>ΔT<sub>c,mean</sub></td><td>-0.3 [1.34]</td><td>-0.31 [1.31]</td><td>-0.3 [1.27]</td><td>-0.28 [1.24]</td><td>-0.27 [1.22]</td><td>-0.28 [1.21]</td></tr><tr><td>ΔT<sub>c,max</sub></td><td>0.3 [2.09]</td><td>0.23 [2.05]</td><td>0.17 [2.03]</td><td>0.1 [2.02]</td><td>0.04 [1.99]</td><td>-0.05 [1.97]</td></tr><tr><td rowspan="3">Temperate</td><td>ΔT<sub>c,min</sub></td><td>-0.71 [1.12]</td><td>-0.66 [1.04]</td><td>-0.62 [0.97]</td><td>-0.61 [0.93]</td><td>-0.6 [0.89]</td><td>-0.61 [0.85]</td></tr><tr><td>ΔT<sub>c,mean</sub></td><td>-0.67 [0.94]</td><td>-0.7 [0.94]</td><td>-0.73 [0.93]</td><td>-0.76 [0.93]</td><td>-0.79 [0.93]</td><td>-0.83 [0.91]</td></tr><tr><td>ΔT<sub>c,max</sub></td><td>-1.72 [2.65]</td><td>-1.83 [2.62]</td><td>-1.94 [2.59]</td><td>-2.04 [2.57]</td><td>-2.12 [2.55]</td><td>-2.21 [2.5]</td></tr><tr><td rowspan="3">Tropical</td><td>ΔT<sub>c,min</sub></td><td>-0.11 [0.76]</td><td>-0.13 [0.74]</td><td>-0.17 [0.71]</td><td>-0.2 [0.7]</td><td>-0.24 [0.67]</td><td>-0.28 [0.65]</td></tr><tr><td>ΔT<sub>c,mean</sub></td><td>-1.12 [0.56]</td><td>-1.16 [0.56]</td><td>-1.2 [0.56]</td><td>-1.24 [0.57]</td><td>-1.29 [0.57]</td><td>-1.34 [0.58]</td></tr><tr><td>ΔT<sub>c,max</sub></td><td>-3.4 [2.33]</td><td>-3.56 [2.26]</td><td>-3.75 [2.19]</td><td>-3.96 [2.12]</td><td>-4.18 [2.03]</td><td>-4.37 [1.95]</td></tr></tbody></table>

## Appendix E. Complementary figures for canopy air temperature ($T_c$)

<figure><img src="image_12.png" /><figcaption>**Figure E1.** Similar to figure 2, but for canopy air temperature.</figcaption></figure>
## Appendix D. Future temperature offsets per degree of global warming

<table><caption>**Table D1.** Soil temperature minimum, mean and maximum offsets (°C) per degree of global mean warming ΔGMT (°C). Standard deviation is given in brackets.</caption><thead><tr><th>ΔGMT</th><th></th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td rowspan="3">Boreal</td><td>ΔT<sub>g,min</sub></td><td>-0.91 [0.94]</td><td>-0.89 [0.92]</td><td>-0.83 [0.91]</td><td>-0.78 [0.89]</td><td>-0.73 [0.89]</td><td>-0.67 [0.89]</td></tr><tr><td>ΔT<sub>g,mean</sub></td><td>-0.57 [0.84]</td><td>-0.57 [0.83]</td><td>-0.54 [0.82]</td><td>-0.53 [0.82]</td><td>-0.51 [0.82]</td><td>-0.49 [0.83]</td></tr><tr><td>ΔT<sub>g,max</sub></td><td>-0.66 [1.12]</td><td>-0.68 [1.12]</td><td>-0.7 [1.13]</td><td>-0.72 [1.13]</td><td>-0.74 [1.13]</td><td>-0.76 [1.14]</td></tr><tr><td rowspan="3">Temperate</td><td>ΔT<sub>g,min</sub></td><td>-0.33 [0.76]</td><td>-0.3 [0.74]</td><td>-0.27 [0.71]</td><td>-0.24 [0.69]</td><td>-0.21 [0.67]</td><td>-0.18 [0.64]</td></tr><tr><td>ΔT<sub>g,mean</sub></td><td>-0.65 [0.5]</td><td>-0.66 [0.5]</td><td>-0.67 [0.51]</td><td>-0.67 [0.52]</td><td>-0.68 [0.52]</td><td>-0.69 [0.53]</td></tr><tr><td>ΔT<sub>g,max</sub></td><td>-1.65 [1.13]</td><td>-1.7 [1.15]</td><td>-1.75 [1.16]</td><td>-1.8 [1.16]</td><td>-1.85 [1.17]</td><td>-1.9 [1.17]</td></tr><tr><td rowspan="3">Tropical</td><td>ΔT<sub>g,min</sub></td><td>0.52 [0.52]</td><td>0.49 [0.5]</td><td>0.44 [0.49]</td><td>0.4 [0.47]</td><td>0.36 [0.46]</td><td>0.32 [0.46]</td></tr><tr><td>ΔT<sub>g,mean</sub></td><td>-0.9 [0.51]</td><td>-0.94 [0.51]</td><td>-0.99 [0.51]</td><td>-1.03 [0.51]</td><td>-1.08 [0.51]</td><td>-1.12 [0.51]</td></tr><tr><td>ΔT<sub>g,max</sub></td><td>-3.47 [1.38]</td><td>-3.57 [1.4]</td><td>-3.67 [1.42]</td><td>-3.77 [1.44]</td><td>-3.87 [1.47]</td><td>-3.96 [1.48]</td></tr></tbody></table>

<table><caption>**Table D2.** Canopy air temperature minimum, mean and maximum offsets (°C) per degree of global mean warming ΔGMT (°C). Standard deviation is given in brackets.</caption><thead><tr><th>ΔGMT</th><th></th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td rowspan="3">Boreal</td><td>ΔT<sub>c,min</sub></td><td>-2.06 [1.98]</td><td>-2.08 [1.94]</td><td>-2.02 [1.84]</td><td>-1.92 [1.74]</td><td>-1.85 [1.65]</td><td>-1.8 [1.61]</td></tr><tr><td>ΔT<sub>c,mean</sub></td><td>-0.3 [1.34]</td><td>-0.31 [1.31]</td><td>-0.3 [1.27]</td><td>-0.28 [1.24]</td><td>-0.27 [1.22]</td><td>-0.28 [1.21]</td></tr><tr><td>ΔT<sub>c,max</sub></td><td>0.3 [2.09]</td><td>0.23 [2.05]</td><td>0.17 [2.03]</td><td>0.1 [2.02]</td><td>0.04 [1.99]</td><td>-0.05 [1.97]</td></tr><tr><td rowspan="3">Temperate</td><td>ΔT<sub>c,min</sub></td><td>-0.71 [1.12]</td><td>-0.66 [1.04]</td><td>-0.62 [0.97]</td><td>-0.61 [0.93]</td><td>-0.6 [0.89]</td><td>-0.61 [0.85]</td></tr><tr><td>ΔT<sub>c,mean</sub></td><td>-0.67 [0.94]</td><td>-0.7 [0.94]</td><td>-0.73 [0.93]</td><td>-0.76 [0.93]</td><td>-0.79 [0.93]</td><td>-0.83 [0.91]</td></tr><tr><td>ΔT<sub>c,max</sub></td><td>-1.72 [2.65]</td><td>-1.83 [2.62]</td><td>-1.94 [2.59]</td><td>-2.04 [2.57]</td><td>-2.12 [2.55]</td><td>-2.21 [2.5]</td></tr><tr><td rowspan="3">Tropical</td><td>ΔT<sub>c,min</sub></td><td>-0.11 [0.76]</td><td>-0.13 [0.74]</td><td>-0.17 [0.71]</td><td>-0.2 [0.7]</td><td>-0.24 [0.67]</td><td>-0.28 [0.65]</td></tr><tr><td>ΔT<sub>c,mean</sub></td><td>-1.12 [0.56]</td><td>-1.16 [0.56]</td><td>-1.2 [0.56]</td><td>-1.24 [0.57]</td><td>-1.29 [0.57]</td><td>-1.34 [0.58]</td></tr><tr><td>ΔT<sub>c,max</sub></td><td>-3.4 [2.33]</td><td>-3.56 [2.26]</td><td>-3.75 [2.19]</td><td>-3.96 [2.12]</td><td>-4.18 [2.03]</td><td>-4.37 [1.95]</td></tr></tbody></table>

## Appendix E. Complementary figures for canopy air temperature (T_c)

<figure><img src="image_12.png" /><figcaption>**Figure E1.** Similar to figure 2, but for canopy air temperature.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure E2.** Similar to figure 3, but for canopy air temperature.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure E3.** Similar to figure 5, but for canopy air temperature. The offset values per global warming degrees are shown in appendix table D2.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure E2.** Similar to figure 3, but for canopy air temperature.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure E3.** Similar to figure 5, but for canopy air temperature. The offset values per global warming degrees are shown in appendix table D2.</figcaption></figure>
ORCID iDs

Gabriel Hes ● https://orcid.org/0000-0002-0408-8463

Inne Vanderkelen ● https://orcid.org/0000-0002-8673-1933

Rosie Fisher ● https://orcid.org/0000-0003-3260-9227

Jérôme Chave ● https://orcid.org/0000-0002-7766-1347

Jérôme Ogée ● https://orcid.org/0000-0002-3365-8584

Edouard L Davin ● https://orcid.org/0000-0003-3322-9330

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ORCID iDs

Gabriel Hes ● https://orcid.org/0000-0002-0408-8463

Inne Vanderkelen ● https://orcid.org/0000-0002-8673-1933

Rosie Fisher ● https://orcid.org/0000-0003-3260-9227

Jérôme Chave ● https://orcid.org/0000-0002-7766-1347

Jérôme Ogée ● https://orcid.org/0000-0002-3365-8584

Edouard L Davin ● https://orcid.org/0000-0003-3322-9330

References

Aguirre-Gutiérrez *J et al* 2020 Long-term droughts may drive drier tropical forests towards increased functional, taxonomic and phylogenetic homogeneity *Nat. Commun.* **11** 3346

Bertrand R, Riofrio-Dillon G, Lenoir J, Drapier J, de Ruffray P, Gégout J-C and Loreau M 2016 Ecological constraints increase the climatic debt in forests *Nat. Commun.* **7** 12643

Bonan G B, Oleson K W, Vertenstein M, Levis S, Zeng X, Dai Y, Dickinson R E and Yang Z-L 2002 The land surface climatology of the community land model coupled to the ncar community climate model *J. Clim.* **15** 3123–49

Bonan G B, Patton E G, Finnigan J J, Baldocchi D D and Harman I N 2021 Moving beyond the incorrect but useful paradigm: reevaluating big-leaf and multilayer plant canopies to model biosphere-atmosphere fluxes—a review *Agric. Forest Meteorol.* **306** 108435

Bonan G B, Patton E G, Harman I N, Oleson K W, Finnigan J J, Lu Y and Burakowski E A 2018 Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0) *Geosci. Model Dev.* **11** 1467–96

Bramer I *et al* 2018 Advances in monitoring and modelling climate at ecologically relevant scales *Adv. Ecol. Res.* **58** 101–61

Danabasoglu G *et al* 2020 The community Earth system model version 2 (CESM2) *J. Adv. Model. Earth Syst.* **12** e2019MS001916

Davin E L and Noblet-Ducodré N d 2010 Climatic impact of global-scale deforestation: radiative Cersus nonradiative processes *J. Clim.* **23** 97–112

De Frenne P *et al* 2013 Microclimate moderates plant responses to macroclimate warming *Proc. Natl Acad. Sci.* **110** 18561–5

De Frenne P *et al* 2021 Forest microclimates and climate change: importance, drivers and future research agenda *Glob. Change Biol.* **27** 2279–97

De Frenne P, Zellweger F, Rodriguez-Sánchez F, Scheffers B R, Hylander K, Luoto M, Vellend M, Verheyen K and Lenoir J 2019 Global buffering of temperatures under forest canopies *Nat. Ecol. Evol.* **3** 744–9

De Lombaerde E *et al* 2022 Maintaining forest cover to enhance temperature buffering under future climate change *Sci. Total Environ.* **810** 151338

De Pauw K *et al* 2022 Forest understory communities respond strongly to light in interaction with forest structure, but not to microclimate warming *New Phytol.* **233** 219–35

Deutsch C A, Tewksbury J J, Huey R B, Sheldon K S, Ghalambor C K, Haak D C and Martin P R 2008 Impacts of climate warming on terrestrial ectotherms across latitude *Proc. Natl Acad. Sci.* **105** 6668–72

Doughty C E *et al* 2023 Tropical forests are approaching critical temperature thresholds *Nature* **621** 1–7

Duveiller G, Hooker J and Cescatti A 2018 The mark of vegetation change on Earth’s surface energy balance *Nat. Commun.* **9** 679

Fisher R A *et al* 2018 Vegetation demographics in earth system models: a review of progress and priorities *Glob. Change Biol.* **24** 35–54

Frey S J, Hadley A S, Johnson S L, Schulze M, Jones J A and Betts M G 2016 Spatial models reveal the microclimatic buffering capacity of old-growth forests *Sci. Adv.* **2** e1501392

Geiger R, Aron R H and Todhunter P 2009 *The Climate Near the Ground* (Rowman & Littlefield)

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Niittynen P, Heikkinen R K and Luoto M 2018 Snow cover is a neglected driver of arctic biodiversity loss *Nat. Clim. Change* 8 997–1001

O'Connor J C, Dekker S C, Staal A, Tuinenburg O A, Rebel K T and Santos M J 2021 Forests buffer against variations in precipitation *Glob. Change Biol.* 27 4686–96

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Qu X and Hall A 2014 On the persistent spread in snow-albedo feedback *Clim. Dyn.* 42 69–81

Richard B *et al* 2021 The climatic debt is growing in the understorey of temperate forests: stand characteristics matter *Glob. Ecol. Biogeogr.* 30 1474–87

Roebroek C T, Duveiller G, Seneviratne S I, Davin E L and Cescatti A 2023 Releasing global forests from human management: how much more carbon could be stored? *Science* 380 749–53

Ryder J *et al* 2016 A multi-layer land surface energy budget model for implicit coupling with global atmospheric simulations *Geosci. Model Dev.* 9 223–45

Sanczuk P *et al* 2023 Microclimate and forest density drive plant population dynamics under climate change *Nat. Clim. Change* 13 1–8

Senior R A, Hill J K, Benedick S and Edwards D P 2018 Tropical forests are thermally buffered despite intensive selective logging *Glob. Change Biol.* 24 1267–78

Simard M, Pinto N, Fisher J B and Baccini A 2011 Mapping forest canopy height globally with spaceborne lidar *J. Geophys. Res.: Biogeosci.* 116 G4

Swenson S C, Burns S P and Lawrence D M 2019 The impact of biomass heat storage on the canopy energy balance and atmospheric stability in the community land model *J. Adv. Model. Earth Syst.* 11 83–98

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Tewksbury J J, Huey R B and Deutsch C A 2008 Putting the heat on tropical animals *Science* 320 1296–7

Trew B *et al* 2023 Novel climates are already widespread beneath the world's tropical forest canopies

Vandewiele M, Geres L, Lotz A, Mandl L, Richter T, Seibold S, Seidl R and Senf C 2023 Mapping spatial microclimate patterns in mountain forests from lidar *Agric. Forest Meteorol.* 341 109662

Vasseur D A, DeLong J P, Gilbert B, Greig H S, Harley C D, McCann K S, Savage V, Tunney T D and O'Connor M I 2014 Increased temperature variation poses a greater risk to species than climate warming *Proc. R. Soc. B* 281 20132612

von Arx G, Dobbertin M and Rebetez M 2012 Spatio-temporal effects of forest canopy on understory microclimate in a long-term experiment in Switzerland *Agric. Forest Meteorol.* 166 144–55

Von Arx G, Graf Pannatier E, Thimonier A and Rebetez M 2013 Microclimate in forests with varying leaf area index and soil moisture: potential implications for seedling establishment in a changing climate *J. Ecol.* 101 1201–13

Williamson J *et al* 2020 Riparian buffers act as a microclimatic refugia in oil palm landscapes *J. Appl. Ecol.* 58 431–42

Zellweger F *et al* 2020 Forest microclimate dynamics drive plant responses to warming *Science* 368 772–5

Zeng X, Shaikh M, Dai Y, Dickinson R E and Myneni R 2002 Coupling of the common land model to the NCAR community climate model *J. Clim.* 15 1832–54