# Where Snow and Forest Meet: A Global Atlas

C. Deschamps-Berger, J. López-Moreno, S. Gascoin, G. Mazzotti, A. Boone

► To cite this version:

C. Deschamps-Berger, J. López-Moreno, S. Gascoin, G. Mazzotti, A. Boone. Where Snow and Forest Meet: A Global Atlas. Geophysical Research Letters, 2025, 52 (10), 10.1029/2024GL113684. hal-05160320

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Distributed under a Creative Commons Attribution 4.0 International License
# Geophysical Research Letters®

## RESEARCH LETTER

10.1029/2024GL113684

**Key Points:**

* A global atlas of the importance of snowfall in forest was calculated at 0.1° resolution from existing reanalysis and tree cover data

* Snow falls in forest over 23% of the land mass, mostly in the boreal region (15.8·10⁶ km²) and elsewhere in mountains (4.9·10⁶ km²)

* Snowfall in forest is up to 23% of the precipitation of large hydrological basins (~10⁵ km²) and reaches a maximum of 64% in 0.1° grid cells

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

C. Deschamps-Berger,
cesar.deschamps-berger@univ-tlse2.fr

**Citation:**

Deschamps-Berger, C., López-Moreno, J. I., Gascoin, S., Mazzotti, G., & Boone, A. (2025). Where snow and forest meet: A global atlas. *Geophysical Research Letters*, *52*, e2024GL113684. https://doi.org/10.1029/2024GL113684

Received 16 NOV 2024
Accepted 11 APR 2025

**Author Contributions:**

**Conceptualization:** C. Deschamps-Berger, J. I. López-Moreno, S. Gascoin, G. Mazzotti, A. Boone

**Data curation:** C. Deschamps-Berger

**Formal analysis:** C. Deschamps-Berger

**Funding acquisition:** J. I. López-Moreno

**Investigation:** C. Deschamps-Berger

**Methodology:** C. Deschamps-Berger, J. I. López-Moreno, S. Gascoin, G. Mazzotti, A. Boone

**Project administration:** J. I. López-Moreno

**Resources:** J. I. López-Moreno

**Software:** C. Deschamps-Berger, S. Gascoin

**Visualization:** C. Deschamps-Berger

**Writing – original draft:** C. Deschamps-Berger

© 2025. The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

<figure><img src="image_2.png" /></figure>

## Where Snow and Forest Meet: A Global Atlas

C. Deschamps-Berger<fnref n="1" />,² Ⓓ, J. I. López-Moreno<fnref n="1" /> Ⓓ, S. Gascoin<fnref n="3" /> Ⓓ, G. Mazzotti<fnref n="4" />,⁵,⁶ Ⓓ, and A. Boone⁷ Ⓓ

**Abstract** The complex interactions between snow cover and forests have implications for ecosystems, water resources and the Earth's climate. However, the geographic distribution of where snow and forest overlap remains poorly known. Here, we evaluate the importance of snowfall over forested environments and its spatial variability at the global scale and a 0.1° spatial resolution, leveraging an existing climatological reanalysis and a satellite tree cover map. We find that 23% of the land mass (30.5·10⁶ km²) experiences snowfall in forested areas, mostly in the boreal forest (15.8·10⁶ km²) and elsewhere in mountains (4.9·10⁶ km²). There, the solid fraction of precipitation in forest is greater than the median value of 11.1% over 15.3·10⁶ km², reaches 64% locally, and 23% when aggregated over hydrological basins larger than 10⁵ km².

**Plain Language Summary** Seasonal snowpack and forest interact in complex ways, with consequences for tree phenology and water resources. Temperate and boreal forests comprise the largest biome on earth and are known to host seasonal snowpack. However, the extent to which seasonal snow occurs in forests remains uncertain. Here, we map where snow meets the forest by computing the proportion of snowfall over forest. We used a global climate reanalysis and satellite tree cover data to derive the fraction of snowfall over forests at 0.1° resolution. It shows that large regions of the world experience snowfall in forest, especially in the northern hemisphere boreal and temperate climates but also in the southern Andes and in less studied climates (Mediterranean or subtropical humid). We find that 23% of the land mass (30.5·10⁶ km²) has a proportion of snowfall in forest greater than 1%, mostly in the boreal region (15.8·10⁶ km²) and elsewhere in mountains (4.9·10⁶ km²). The median proportion is 11% but can reach up to 64% locally and 23% when averaged over large hydrological basins exceeding 10⁵ km².

## 1. Introduction

Snow has implications for water resources (Barnett et al., 2005; Van Meerveld & Seibert, 2024) and affects the Earth's surface radiative budget by altering land surface albedo (Thackeray et al., 2014; Wang et al., 2016). It falls over a wide range of environments, including temperate and boreal forest which are among the largest terrestrial biomes. The impacts of snow on forests are many, for example, damage to branches by heavy snowfall (Nykänen et al., 1997), root protection through thermal insulation (Sanmiguel-Vallelado et al., 2021), and tree growth fostered by snowmelt water in spring (Zhang et al., 2019). Conversely, trees alter the interactions between the snowpack and the atmosphere by intercepting snowfall in their branches and modifying radiative fluxes, wind patterns and the snowpack albedo. As a consequence, the distribution and evolution of snowpack in forests are distinct from those in an open-environment (Lundquist et al., 2013; Varhola et al., 2010). Forests have diverse net effects on the snowpack below the canopy, resulting in opposite consequences such as increased or decreased accumulation and longer or shorter snow cover duration. These effects arise from the complex interactions between the local climate and the tree species, the tree cover density and the foliage density (Lundquist et al., 2013). For instance, the interception of snowfall by branches is more efficient for coniferous than deciduous species and for warmer temperatures (Lundquist et al., 2021). The climatic conditions further modulate these processes as trees decrease shortwave radiation transmission and increase longwave radiation emission reaching the snowpack. Depending on the solar angle (i.e., the latitude and the temporality of the snow season), the net impact of these processes can enhance or reduce the melt rates (Varhola et al., 2010). Thus, understanding snow-forest interactions requires considering the climatic conditions and forest type.

<footnote n="1">¹Pyrenean Institute of Ecology, Zaragoza, Spain, ²GEODE, UMR, CNRS, University of Jean-Jaurès, Toulouse, France,</footnote>

<footnote n="3">³CESBIO, Université de Toulouse, CNRS/CNES/IRD/INRA/Univ Toulouse 3 Paul Sabatier, Toulouse, France,</footnote>

<footnote n="4">⁴Laboratory of Hydraulics, Hydrology and Glaciology (VAW), Zurich, Switzerland, ⁵Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Sion, Switzerland, ⁶IGE, Université Grenoble-Alpes, INRAE/CNRS/IRD/Grenoble INP, Grenoble, France, ⁷CNRM-Université de Toulouse, Météo-France/CNRS, Toulouse, France</footnote>
Writing - review & editing:

C. Deschamps-Berger, J. I. López-Moreno, S. Gascoin, G. Mazzotti, A. Boone

A global assessment of where snow-forest interactions matter is needed to identify affected regions, their cli- mates, and to guide model development and future data acquisition strategies. However, at the global scale, the exact distribution of seasonal snowpack in forested environments remains uncertain, as most remote sensing techniques, usually applicable elsewhere, are prone to substantial errors in forest (Gascoin et al., 2024). The most commonly used estimate of the forests covered with snow states that about 19% of the northern hemisphere's snow cover overlaps with boreal forest (Rutter et al., 2009). This estimate was obtained assuming a complete overlap of the boreal forest (8.9·10⁶ km²) with snow at the maximal extent of the snow cover in the northern hemisphere (47·10⁶ km²). The snow cover extent was calculated from satellite optical images from 1972 to 1999 (Robinson & Frei, 2000), and therefore did not include recent climatic conditions. In addition, the boreal forest cover estimate solely included evergreen needleleaf boreal forest (Secretariat of the Convention on Biological Diversity, 2001), thereby leaving out other forested environments known to experience snowfall, such as deciduous needleleaf boreal forest, and forest in temperate (Dickerson-Lange et al., 2023; Sicart et al., 2023) or mediterranean (Fayad et al., 2017; Huerta et al., 2019; Sanmiguel-Vallelado et al., 2022) climate zones. A global classification of the snowpack types based on temperature, precipitation and land cover found that snow in forests might occur over a total of 18.2·10⁶ km², of which 12.2·10⁶ km² correspond to boreal forest, while the remaining 6.0·10⁶ km² are mountain forests (Sturm & Liston, 2021). However, other snowpack types described in that study overlap with forests, such as maritime snowpacks. Besides, the importance of the overlap of snow and forest was not quantified in the aforementioned studies since both relied on a discrete characterization of the snowpack, that is, the snow cover area in Rutter et al. (2009), and on a handful of snowpack categories in Sturm and Liston (2021). Thus, determining where snow in forest matters most is currently impossible.

With the recent availability of global climatic data sets and increased access to optimized cloud computing ca- pabilities, the distribution of snow in forests can be revisited and updated. In this study, the intensity of snowfall in forests is computed at 0.1° resolution using the ERA5-Land meteorological reanalysis, and a forest cover map derived from the Landsat archive. The importance of snowfall in forest was determined for different climates as defined in the Köppen–Geiger classification. This classification aims at describing the vegetation types based on climatological criteria (Köppen, 1936), and as such, it samples the main factors governing snow-forest in- teractions. Finally, the importance of snow in forests is also aggregated per hydrological basin and mountain range to identify where snow in forests matters for relevant geographic units.

2. Materials and Methods

The importance of snow-forest interactions was characterized as the fraction of snowfall over forest (FSF) expressed as a percentage. It was estimated as the ratio, for a given pixel, of the snowfall (S) to the total pre- cipitation (Idem, P) multiplied by the tree cover fraction ($f_{\text{forest}}$):

$$FSF = \frac{S}{P} \times f_{\text{forest}} \times 100 \qquad (1)$$

The total precipitation includes rain and snow over forest and over open terrain. The rainfall and snowfall amounts were calculated from the ERA5-Land reanalysis at a monthly time-step between September 1990 and August 2020 for the northern hemisphere and between March 1990 and February 2020 for the southern hemisphere (Muñoz-Sabater, 2019). The winter-spring FSF was calculated for December to May in the Northern Hemisphere and June to November in the Southern Hemisphere. ERA5-Land precipitation fields are linearly interpolated at ~9 km from the ERA5 fields at ~31 km resolution (Muñoz-Sabater et al., 2021). The latter are modeled by the ECMWF Integrated Forecasting System which accounts for precipitation due to large-scale change in atmo- spheric properties and convection (Hersbach et al., 2020). The amount of solid and liquid precipitation are prognostic variables of this atmospheric model which allows complex mixed-phase clouds. The tree cover was calculated for the year 2000 from a time-series of optical images of the Landsat archive at 30 m resolution (Hansen et al., 2013). It was defined as the fraction of a pixel covered by a tree canopy taller than 5 m. Bushes, shrubs, young and small trees were thus not included in this study. The importance of snow on such land covers remains to be studied as they comprise vast areas in the Arctic region, in Mediterranean environments, and in the vicinity of forests where transition to low-height vegetation is progressive (García Criado et al., 2020; López- Moreno et al., 2024; Myers-Smith et al., 2011). The tree cover was aggregated at the ERA5-Land resolution (0.1°) by taking the mean of 1,000 randomly selected pixels of the original product previously averaged at an optimal
resolution by Earth Engine to reduce the computational cost (Figure S1 in Supporting Information S1). These calculations were made on the Earth Engine platform to benefit from optimized cloud and parallel computing (Gorelick et al., 2017). Pixels with a FSF greater than 1% were considered as experiencing snowfall in forest.

The FSF distribution was analyzed for each Köppen-Geiger climate to infer the types of forest (deciduous, coniferous) and of snow-forest interactions occurring. Additionally, the area experiencing snowfall in forest of each climate was calculated. The Köppen-Geiger classification distinguishes 32 climates based on the main climate type (e.g., temperate or continental), the precipitation seasonality (e.g., dry winter or dry summer) and the temperature regime (e.g., hot summer or cold winter), resulting in each climate being identified by a 3-letter code (Köppen, 1936). A Köppen-Geiger climate map at 0.08° for the period 1986 to 2010 was used (Kottek et al., 2006; Rubel et al., 2017) and aggregated for calculation at 0.1° resolution by taking the mode.

The FSF was averaged for the hydrological basins of level 3 in the HydroBASIN data set (Lehner & Grill, 2013) to highlight where snow in forest has a strong hydrological impact at large scale. The level 3 of HydroBASIN represents the largest river basins of each continent with a typical area between $10^5$ km² and $5 \cdot 10^6$ km². The FSF was also averaged over the mountainous terrain of each basin. We used the mountain ranges from the Global Mountain Biodiversity Assessment inventory, which is based on topographic criteria (Snethlage, Geschke, Spehn, et al., 2022; Snethlage, Geschke, Ranipeta, et al., 2022). Hydrological basins and mountain range polygons were simplified to reduce the calculation time.

The area experiencing snowfall in forest was compared to (a) the area of all the land masses on Earth excluding Antarctica and Greenland ($132.8 \cdot 10^6$ km²), (b) the land masses of North America and Eurasia ($73.9 \cdot 10^6$ km²), and (c) the mean snow cover area of the northern hemisphere in winter-spring ($35.4 \cdot 10^6$ km²). The boreal region is where boreal climates are found above 48°N. The land areas were extracted from the HydroBASIN data set. The snow cover area was extracted over the 1990-2020 period for the months from December to May from various satellite time series (e.g. Meteosat, GMS, Aqua/Terra; Estilow et al., 2012). The Copernicus GLO30 digital elevation model resampled at 0.1° was used for the analysis of the FSF distribution per elevation.

## 3. Results

### 3.1. Global Distribution of Snowfall in Forest

We find that snowfall and forest overlap significantly across $30.5 \cdot 10^6$ km², which represents 23% of the total land surface area (Figure 1). The FSF distribution is positively skewed with a median of 11.1% and 75th and 99th percentiles of 18.9% and 38.8%. Regions with FSF greater than the median, the 75th and the 99th percentiles cover respectively $15.3 \cdot 10^6$ km², $7.7 \cdot 10^6$ km², and $0.3 \cdot 10^6$ km². We find that 98% ($30.0 \cdot 10^6$ km²) of this area is in the Northern Hemisphere, in particular in Eurasia and North America with marginal extents in the north of Africa. The median FSF value for the northern hemisphere is 11.2%, excluding pixels with FSF lower than 1%. That corresponds to $15.1 \cdot 10^6$ km² having a FSF of 11.2% or higher, that is 20% of Eurasia and North America and 43% of the mean winter-spring snow cover area. A belt where FSF is typically between 20% and 40% follows the boreal forest distribution in the Northern Hemisphere generally between 50°N and 65°N with more southern reach in the east of North America, as low as 45°N. The variability of the forest cover explains most of the FSF variability compared to the snowfall fraction variability (Figures S2-S4 in Supporting Information S1). This is illustrated with the pattern of FSF between the Ural Mountains and the Central Siberian Plateau. Mid-latitude mountain ranges (e.g., the Alps, the Carpathians, the Caucasus) also show significant FSF, typically up to 20%. Hot spots of high snowfall in forests lie in mountains along the Pacific coast of North America (Pacific Coast Ranges, Rocky Mountains) and in the Kamchatka Peninsula, where FSF reaches between 50% and 64%, the maximum FSF value found. Regions with snowfall in forest are scarcer in the Southern Hemisphere ($0.42 \cdot 10^6$ km²), with the exception of Patagonia in the Southern Andes ($0.30 \cdot 10^6$ km²) where FSF is up to 42%, and marginally in southern Australia, Tasmania and New Zealand (FSF < 15%). We find that $8.1 \cdot 10^6$ km² of mountains experience snowfall in forest, of which $4.9 \cdot 10^6$ km² are out of the boreal region. The FSF increases with elevation in three selected regions dominated by mountains (Figure S5 in Supporting Information S1). It reaches a maximum median FSF between 1,000 m a.s.l. and 1,250 m a.s.l. in the mountain ranges of western North America and Patagonia, and higher, between 1,750 m a.s.l. and 2,000 m a.s.l. for European mountains. The difference in optimal FSF elevation stems from the gradient in snowfall fraction which is steeper at low elevation in North America and Patagonia, while the tree cover gradient is similar in all three regions (not shown).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Fraction of the total precipitation that falls as snow over forest (FSF) obtained as the product of the forest cover (Figure S2 in Supporting Information S1) and the snowfall fraction (Figure S3 in Supporting Information S1). The white divides show the hydrological basins. The main geographical elements commented in the text are indicated with letters.</figcaption></figure>

## 3.2. Climatic Distribution of Snowfall in Forest

The climates where snowfall in forest is most common are temperate (C) and continental (D) with generally higher FSF for the latter (Figure 2 and S6). Snowfall over forest is also marginally found in dry (B) and tundra climates (ET). The precipitation regime is correlated with the FSF distribution with dry-winter conditions (−w) being associated with smaller FSF compared to wet all year (−f) and summer-dry regimes (−s). The temperature regime within a given main climate has the largest impact on FSF distribution, with increasing FSF values with decreasing temperature for temperate climates, while intermediate temperature (−b, −c) show the highest FSF for continental climates compared to the warmest (−a) and coldest (−d). The subset of continental climates corresponding to boreal environments (Dwd, Dfd, Dsd, Dwc, Dfc, Dsc) show the largest extent experiencing snowfall in forest with 16.3·10⁶ km², which is 54% of the area with FSF greater than 1%. The majority of this area

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Area covered by Köppen–Geiger climates per band of fraction of snowfall in forest (FSF) (a). Distribution of area experiencing snowfall in forest (b). Distribution of fraction of the FSF per Köppen–Geiger climate class (c). Only climates with area over 10³ km² are shown.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Fraction of the total precipitation that falls as snow over forest per hydrological basin (white contour) and mountain range (black contour). The main basins commented in the text are indicated by letters.</figcaption></figure>

correspond to the boreal region (15.8·10⁶ km²). Boreal climates notably exhibit high values of FSF, between 10% and 40%. The class of low FSF (1%–10%) shows the largest diversity of climates with 70% not being associated with boreal conditions, while 62% of the area with FSF of 10% or more are associated with boreal conditions. The non-boreal conditions are, however, dominated by hemiboreal (i.e., transition from boreal to temperate, Dfb, Dwb, Dsb) which is 22% of the area with FSF between 1% and 10%. The humid subtropical climate (Cfa) and oceanic climate (Cfb) found in the east of North America also represent an important share of the area with low FSF, with 3.13·10⁶ km² essentially found in the low FSF class (1%–20%). The fractions of these two climates become negligible for higher FSF with less than 2% of the areas with FSF higher than 20%. Mediterranean climates (Csa, Csb, Csc), found north of the Mediterranean Sea, on the Pacific coast of North America, and in the Southern Andes, have a smaller area (0.77·10⁶ km², 3%) also with a FSF smaller than 20%. However, some of the highest FSF (99th percentile) are also associated with these climates in the Sierra Nevada (California). Finally, 0.46·10⁶ km² of monsoon-influenced climates (Cwa, Cwb, Cwc) are also experiencing snowfall in forest in eastern Asia.

### 3.3. Hydrological Importance of Snowfall in Forest

There are 57 basins, out of 272, where snowfall in forest is greater than 1%, covering a total surface area of 39.6·10⁶ km² with a mean FSF of 9% (Figure 3, Table S1 in Supporting Information S1). The highest proportion of snowfall in forest is found in basins which overlap largely with the boreal forest, in which basin-scale FSF lies between 15% and 23%. The west coast of North America shows the highest basin-scale FSF. For instance, the Columbia River Basin has a relatively high basin-scale FSF (21%) over 0.7·10⁶ km² with a FSF of 25% in the mountainous part of the basin. Lower latitude basins exhibit lower FSF, with basin-average values below 10%. Snowfall in forest tends to be more important in the mountainous regions which is explained by a systematically higher snowfall proportion and generally higher forest cover than in the rest of the basin. For instance, the Danube river basin (Europe) has an overall FSF of 6%, but the mountain region of the basin has a value which is twice as large (12%), due to both a higher tree cover (+24%) and a higher snowfall fraction (+6%) in the mountains. Lower FSF is found in the mountains of some high latitude basins which have a lower tree cover in mountains than in the rest of the basin covered by dense boreal forest, as is the case for the Lena and the Yenisei river basins (Asia). Their basin-scale FSF is, respectively, 14% and 17% compared to 13% and 12% for their mountainous region.
### 3.4. Winter-Spring FSF

The winter-spring FSF is larger than the annual FSF as the fraction of snowfall in winter is larger and the forest cover is constant (Figure S7 in Supporting Information S1). The winter-spring increase of the FSF depends on the precipitation regime, as the snowfall fraction seasonality (S/P in Equation 1) depends essentially on the precipitation seasonality (P), assuming most snowfall (S) occurs in winter-spring. For instance, the median winter-spring FSF per climate is generally larger by a factor of two or more than annual FSF for climates with a relatively dry winter (–w), and by a factor close to two for climates with no dry season (–f, i.e. rather homogeneous precipitation all year round). For climates with a relatively dry summer (–s, e.g. Mediterranean climates), annual and winter-spring FSF are similar (Figure S8 in Supporting Information S1). Overall, the median winter-spring FSF is 19.9%; a factor 1.8 times greater than the annual median FSF of 11.1%. The areas with the highest values of winter-spring FSF (i.e., 99th percentile, 61.8%) generally match the areas with the highest annual FSF.

### 4. Discussion

The global map of the FSF confirms that boreal environments are the main biome where snowfall occurs over forests. This area (15.8·10⁶ km²) is larger than previous estimates of 8.9·10⁶ km² (Rutter et al., 2009) and of 12.2·10⁶ km² (Sturm & Liston, 2021). The discrepancy with Rutter et al. (2009) might come from the definition of the boreal forest which they estimate to be 8.9·10⁶ km² fully covered with snow while the Köppen-Geiger climate used here has a much larger area associated with boreal conditions (18.4·10⁶ km²). Part of the difference in area comes from the exclusion of deciduous boreal forest in Rutter et al. (2009) and from the definition of forest as areas with a minimal tree cover (e.g., 10% for the Food and Agriculture Organization). The boreal definition used here also differs from Sturm and Liston (2021). For instance, most of the Scandinavian peninsula and parts of west Siberia are classified as montane forest and tundra in Sturm and Liston (2021) while they are associated with the boreal environment in the Köppen-Geiger map. The sum of the montane and boreal forest areas experiencing seasonal snow in Sturm and Liston (2021) (18.2·10⁶ km²) remains lower than the total area estimated here (30.5·10⁶ km²). However, our estimate depends on the minimum FSF threshold which was set to 1%. If set to 5% or 10%, the total area drops to 22.2·10⁶ km² and 16.4·10⁶ km², thus better matching the estimate of Sturm and Liston (2021). Besides, the continuous nature of the FSF enables us to quantify the variability of the importance of snowfall over forest. It highlights hotspots such as the west coast of North America, the Kamchatka peninsula and the Central Siberian Plateau. About 15% of the area experiencing snowfall in forest occurs in temperate climates where deciduous forests are frequent (i.e., oceanic, mediterranean and monsoon influenced climates). This emphasizes the need to study snow and forest interactions in these climates and forest types which are distinct from more frequently studied boreal environments and coniferous forests (Huerta et al., 2019; López-Moreno et al., 2024).

The FSF is an indicator focusing on snowfall precipitation and which, as such, can only indirectly inform about the snow-forest interactions. For instance, even a low FSF could lead to a large meteorological and climatic impact as even little snow intercepted in the tree branches can modify the surface albedo. The FSF rather represents the maximal potential importance of snow in forest from a hydrological perspective. The amount of snowfall over forests is indeed distinct from the amount of snow stored under the canopy due to sublimation and melt of the snow intercepted in the branches.

The uncertainty of the FSF results from uncertainties in the native snowfall fraction, in the tree cover products, and from our processing. We assume that the error of the tree cover map at 0.1° is low as it is resampled from a much higher resolution product (30 m) (Figure S1 in Supporting Information S1). ERA5-Land is a state-of-the-art meteorological reanalysis. It provides reliable temperature (Dutra et al., 2020), precipitation amounts (Gebrechorkos et al., 2024; Hassler & Lauer, 2021) and snow cover properties (Kouki et al., 2023; Monteiro & Morin, 2023; Muñoz-Sabater et al., 2021). The uncertainty of the phase of the precipitation and thus of the snowfall fraction of ERA5-Land is less well known but is expected to be similar to the uncertainty of ERA5 as its meteorological fields are used as input for ERA5-Land production (Muñoz-Sabater et al., 2021). The phase of the precipitation of ERA5 showed good agreement with in situ observation at global scale (Xiong et al., 2022). Our approach assumes a homogeneous sub-pixel distribution of the snowfall fraction and of the tree cover, which might lead to overestimation of the actual FSF. Snowfall fraction data at higher spatial resolution would be required to reduce this error. It also assumes that snow is falling vertically which underestimates the snowfall over trees in windy conditions (Hedstrom & Pomeroy, 1998).
Many studies have focused on snow-forest interactions at small or local scale to accurately describe the impact of trees on the snowpack evolution (Dickerson-Lange et al., 2017; Malle et al., 2019). Improved modeling of the snowpack in forest is achieved by considering the fine 3D structure of the forest (Mazzotti et al., 2020). However, at a larger scale, land surface and climate modeling struggle to reproduce the distribution of snow over forested areas (Wang et al., 2016). The representation of snow interception by tree canopies has a large impact on the Earth's albedo and results in large discrepancies and uncertainties between models (Lundquist et al., 2021; Thackeray et al., 2014). The assimilation of remote sensing data in models is hindered as most satellite products that would enable continent-scale observation of snow in forests have large errors (Gascoin et al., 2024; Largeron et al., 2020). However, promising results to map snow depth in forests are emerging from satellite laser altimetry owing to its ability to penetrate the canopy (Besso et al., 2024; Deschamps-Berger et al., 2023) and from L-band InSAR to map SWE changes in forest (Bonnell et al., 2024). The FSF atlas presented here is valuable to define what observation system will be required to obtain global observations of snow properties in forest and to determine what regions and climates should be focused on to improve climate modeling.

The forest cover and the snowfall fraction are rapidly changing due to human-induced land cover and climate changes. Forest cover has increased due to afforestation and natural forest expansion following abandonment of agricultural land and climatic shift, meanwhile losses essentially occur due to deforestation, wind throw, wildfires and biotic outbreaks (Song et al., 2018). Between 2000 and 2012, the boreal forest cover was found to have decreased by $-0.4 \cdot 10^6$ km² when including all tree cover, which corresponds to a yearly decrease of $-0.15\%$ of its 2000 area (Table S1 in Hansen et al. (2013)). The losses exceed the gain by a factor of three and 70% are due to fires. This is a large source of uncertainty for the future evolution of forests as burnt areas were found to have largely increased in Eurasian boreal forests between 2001 and 2019 (Tyukavina et al., 2022) and in Canada between 1959 and 2015 (Hanes et al., 2019). Furthermore, climate change will shift boreal conditions northward, converting polar climate to boreal in the north and boreal climate to temperate climate in the south (Beck et al., 2023). This should, however, have little impact on the overall boreal climate distribution (<1%), and result in even more limited forest gain due to the slower forest expansion (Cui et al., 2021; Rees et al., 2020). The temperate forest cover was found to have decreased by $-0.1 \cdot 10^6$ km² between 2000 and 2012, thus at a slower rate ($-0.04\%$ per year) than boreal forest (Hansen et al., 2013). The losses are less caused by fire but more by disturbance from weather, biotic and human driver (Hansen et al., 2013; Tyukavina et al., 2022). Gains occurred in Europe and the east of North America following the rural decline of the past century (Winkler et al., 2021) or as a consequence of planned afforestation, as in China (Peng et al., 2014). Mid-latitude mountain forests are also expanding upward with increasing tree-line altitude (Lu et al., 2021). Although this area gain represents a small fraction of the temperate forest cover, it is where snowfall fractions are the highest, meaning potential high-FSF areas.

The snowfall proportion, the other component of the FSF, is also rapidly changing in the face of climate change. The snow cover surface area is shrinking over mid-latitude low-elevation mountains due to increasing air temperature (Bormann et al., 2018; Hock et al., 2019; Notarnicola, 2020). At higher latitudes, where boreal climates are found, the trends are more contrasted and sometimes even positive, driven by precipitation changes (Pulliainen et al., 2020). Climate change will also likely modify the importance of the processes governing snow-forest interactions. A warmer climate increases snow interception by branches, but past a certain threshold, also leads to increased unloading from the branches, with direct consequences for the sub-canopy snow mass balance and the overall land surface albedo (Lundquist et al., 2021). Thus, in the future, the FSF is expected to decline due to the general loss of snow cover with exceptions in some regions due to tree cover and precipitation increases. The past evolution of the forest and of the snow cover were not considered here. This would require using lower resolution and more uncertain forest cover data sets than the one used here (Song et al., 2018; Winkler et al., 2021) or assessing climate change over a short baseline, preventing the best practice of defining a climate over 30-year periods (Monteiro & Morin, 2023).

## 5. Conclusion

The global atlas of the FSF provides an extensive mapping of where and how much snow falls over forests in the current climate, highlighting regions where forest snow is expected to have high climatic, ecological and hydrological impacts. Snowfall in forests occurs over 23% of the land mass ($30.5 \cdot 10^6$ km²), mostly in the boreal forest ($15.8 \cdot 10^6$ km²) and elsewhere in mountain ranges ($4.9 \cdot 10^6$ km²). Although less frequent in the southern hemisphere, it is common in Patagonia ($0.30 \cdot 10^6$ km²). It is associated with boreal and hemiboreal climates but
also occasionally with subtropical humid, Mediterranean and monsoon influenced climates. This study has determined that snowfall is likely of hydrological importance for 57 large basins covering a total of 39.6-10⁶ km², which experience a mean FSF of 9%. These results will help develop strategies to monitor forest snow globally and guide model development and evaluation.

## Data Availability Statement

The codes used in this work are available at https://framagit.org/cesardb/forest_snow_world.git. The data set produced are available via Deschamps-Berger (2024) and can be explored at https://cesar-db.github.io/forest_snow_world_map/.

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## Acknowledgments

CDB acknowledges funding from the Spanish Ministry of Science and Innovation (Juan de la Cierva: FJC2021-047268-I, MARGISNOW: PID2021-124220OB-100) and from the French Space Agency (Centre National d'Etudes Spatiales, CNES 26-364) postdoctoral fellowship. GM acknowledges funding from the Swiss National Science Foundation (P5R5PN_225378). The authors thank the two reviewers and the editor for their insightful comments and suggestions.
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