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# Asymmetric influence of forest cover gain and loss on land surface temperature

Yongxian Su, Chaoqun Zhang, Philippe Ciais, Zhenzhong Zeng, Alessandro

Cescatti, Jiali Shang, Jing M. Chen, Jane Liu, Yingping Wang, Wenping

Yuan, et al.

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► To cite this version:

Yongxian Su, Chaoqun Zhang, Philippe Ciais, Zhenzhong Zeng, Alessandro Cescatti, et al. Asymmetric influence of forest cover gain and loss on land surface temperature. Nature Climate Change, 2023, 13 (8), pp.823-831. 10.1038/s41558-023-01757-7. hal-04190815

HAL Id: hal-04190815

https://hal.science/hal-04190815v1

Submitted on 3 Oct 2025

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1 **Asymmetric influence of forest cover gain and loss on land surface temperature**

2 Yongxian Su<sup>1,2</sup>, Chaoqun Zhang<sup>1,2</sup>, Philippe Ciais<sup>3</sup>, Zhenzhong Zeng<sup>4</sup>, Alessandro

3 Cescatti<sup>5</sup>, Jiali Shang<sup>6</sup>, Jing Ming Chen<sup>7</sup>, Jane Liu<sup>7</sup>, Ying-Ping Wang<sup>8</sup>, Wenping Yuan<sup>1</sup>,

4 Shushi Peng<sup>9</sup>, Xuhui Lee<sup>10</sup>, Zaichun Zhu<sup>9</sup>, Lei Fan<sup>11</sup>, Xiaoping Liu<sup>1</sup>, Liyang Liu<sup>1,2,3</sup>,

5 Raffaele Lafortezza<sup>12,13</sup>, Yan Li<sup>14</sup>, Jiashun Ren<sup>1,2</sup>, Xueqin Yang<sup>1,2</sup>, Xiuzhi Chen<sup>1</sup>

6 1 Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle,

7 Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies,

8 School of Atmospheric Sciences, School of Ecology, School of Geography and

9 Planning, Sun Yat-sen University, Guangzhou, China;

10 2. Guangdong Provincial Key Laboratory of Remote Sensing and Geographical

11 Information System, Guangdong Open Laboratory of Geospatial Information

12 Technology and Application, Guangzhou Institute of Geography, Guangdong Academy

13 of Sciences, Guangzhou, China;

14 3. Laboratoire des Sciences du Climat et de l'Environnement, IPSL, CEA-CNRS-

15 UVSQ, Université Paris-Saclay, Gif sur Yvette, France;

16 4. School of Environmental Science and Engineering, Southern University of Science

17 and Technology, Shenzhen, China;

18 5. European Commission, Joint Research Centre, Ispra, Italy;

19 6. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada,

20 Ottawa, Ontario, Canada;

21 7. Department of Geography and Planning, University of Toronto, Toronto, M5S 3G3,

22 Ontario, Canada;

23 8. CSIRO Environment, Aspendale, Victoria, Australia;

24 9. College of Urban and Environmental Sciences, Peking University, Beijing, China;

25 10. School of the Environment, Yale University, New Haven, CT, USA;

26 11. School of Geographical Sciences, Southwest University, Chongqing, China;

27 12. Department of Soil, Plant and Food Sciences, University of Bari "Aldo Moro", Via

28 Amendola 165/A, 70126 Bari, Italy;

29 13. Beijing Forestry University, Beijing 100083, China;

30 14. State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing
31 Normal University, Beijing, 100875, China

32

33 **Corresponding author:** X. Chen (chenxzh73@mail.sysu.edu.cn)

34

35 **Abstract**

36 In recent decades, forests globally experienced tree cover gains and losses, often in
37 a fine-scale process. While the biogeochemical implications of such changes on
38 carbon storage are recognized, the direct biophysical effects of fine-scale tree cover
39 gains and losses on land surface temperature are less well understood. Here we
40 show how land surface temperature at 0.05° resolution grid cell responds to sub-
41 grid gross tree cover changes. We find that in tropical forests, the biophysical
42 cooling induced by enhanced evapotranspiration per unit area of tree cover gain
43 is greater in magnitude than the warming from tree cover loss. The goal of no
44 biophysical warming effects from tree cover changes could be achieved by
45 regaining > 75% of previously lost tree cover areas. In temperate forests, this
46 minimum fraction increases to 83%. In boreal forests, however, the biophysical
47 cooling from per unit area of tree cover loss due to increasing albedo outweighs
48 the warming from tree cover gain, and a tree cover loss should be greater than 66%
49 of tree cover gain to achieve the goal of zero local biophysical warming. Neglecting
50 this asymmetric temperature effect of fine-scale tree cover gain and loss ignores
51 the fact that biophysical feedbacks continue to cause surface temperature changes
52 even under net-zero tree cover changes. Thus, it is necessary to account for gross,
53 rather than net, tree cover changes when quantifying the biophysical effects of
54 forests.

55

56 **Introduction**

57 Forests store 45% of terrestrial carbon and remove from the atmosphere a large
58 amount of carbon dioxide released by human activities to mitigate global warming¹,².
59 This process leads to a global biogeochemical cooling effect by reducing the radiative
60 forcing of carbon dioxide³. In addition, forests influence the land-atmosphere exchange
of energy and water⁴⁻⁹ and exert direct biophysical effects on global surface temperatures through radiative processes (albedo)¹⁰ and nonradiative processes (latent and sensible heat fluxes)¹¹⁻¹³. Forests also have indirect biophysical feedbacks on climate through atmospheric coupling, e.g. atmospheric circulation, cloud formation, and precipitation⁴,¹⁴. During the twenty-first century, forests have experienced dramatic changes (i.e., land cover conversions and tree cover changes in forests remaining forests)¹⁵,¹⁶, affecting their biogeochemical trace gases exchange¹¹ and biophysical processes⁵,⁹,¹⁷⁻¹⁹.

At the global scale, studies on biogeochemical¹¹,²⁰,²¹ and biophysical temperature effects²²⁻²⁴ of forests have mainly focused on land cover change such as afforestation and deforestation. The biogeochemical effect is quantified by calculating the carbon difference between forest and neighboring nonforest grid cells, which is then converted to a global temperature change. The biophysical effect is quantified by interpreting spatial differences in temperature, mainly satellite-based land surface temperature (LST)²¹. These approaches rely on the space-for-time analogies where spatial gradients in carbon storage or LST between forest and neighboring nonforest are used as proxies for estimating temporal changes of biogeochemical or biophysical effects⁷,²⁵.

However, significant fine-scale tree cover changes (gains and losses) have occurred in forests remaining forests worldwide¹⁵, mainly due to natural disturbance, forest management, and other changes in canopy density²⁶,²⁷. Although these tree cover gains and losses in established forests are not a land cover conversion, they can still impact the global carbon balance²⁸,²⁹. High-resolution satellite carbon data have been used to assess such biogeochemical implications induced by fine-scale tree cover changes based on the space-for-time analogy method¹¹,²⁰,²¹. Nevertheless, challenges remain for quantifying the direct biophysical LST effects induced by fine-scale tree cover changes⁵,¹⁸,³⁰⁻³⁶. This is an important research question because LST is monitored globally from satellites with frequent revisits only at a kilometric-scale spatial resolution whereas changes in tree cover can be assessed at a finer scale of 30 meters.
Alkama and Cescatti developed a methodology to estimate LST change caused by direct biophysical effects of 30-m resolution net tree cover changes using satellite-based time-
series data⁵, known as the time-series analysis method³⁷. This approach was, however, 91 only applied to net changes in tree cover⁵,³⁰ and did not investigate the distinct 92 biophysical effects caused by gross tree cover gain and loss within a LST grid cell. 93

To address this knowledge gap, we focused on forest areas that had undergone fine-scale gross tree cover changes while not changing land cover (hereinafter referred to as 94 sub-grid tree cover gain and loss with respect to the coarser scale LST observations). 95 We aimed to quantify the differences of direct biophysical LST effects induced by sub-grid tree cover gains versus losses from 2000 to 2012. To do so, we first selected 0.05° 96 forest grid cells that were classified as “forestlands” on the Moderate Resolution 97 Imaging Spectroradiometer (MODIS) land cover product³⁸,³⁹. We then classified a 98 forest grid cell as “disturbed” if it experienced more than 2% of sub-grid change in tree 99 cover gain ($f_{gain}>0.02$) or loss ($f_{loss}>0.02$) based on 30-m resolution tree cover maps 100 from Global Forest Watch (GFW)¹⁵ during 2000-2012. Correspondingly, we classified 101 a forest grid cell as “undisturbed” if it experienced less than 2% of gross tree cover 102 change (i.e., $f_{gain}≤0.02$ and $f_{loss}≤0.02$) and showed a stable Normalized Difference 103 Vegetation Index ($ΔNDVI=0±0.02$). We used the 0.05° resolution MOD11C3 v061 104 LST product³⁸ to calculate the LST anomaly between 2000 and 2012 for each forest 105 grid cell. Finally, the LST anomaly of neighboring undisturbed grid cells caused by 106 climate variability alone ($ΔLST_{cv}$) was removed from the signals of the disturbed grid 107 cells to extract the direct biophysical effects on LST caused by sub-grid tree cover 108 changes ($ΔLST_{fc}$), following the methodology of Alkama and Cescatti⁵ adapted with 109 more stringent criteria (see Methods for details). Only those disturbed grid cells with 110 more than 5 reference undisturbed grid cells within a 50-km distance⁵ were included in 111 the analysis. 112

## Main

### Global patterns of $ΔLST_{fc}$ induced by tree cover changes in forests

Globally, 34.7% of all the 0.05° forest grid cells were classified as disturbed forests, 118 with 10.3% being located in tropical regions, 6.8% in temperate regions, and 17.6% in 119
boreal regions (Fig. 1a). Approximately 57.4% of these disturbed forest grid cells, mainly in tropical (20°N-20°S) and boreal regions (Canada and eastern Russia), experienced a net tree cover loss defined by $f_{net}=f_{gain}-f_{loss}<-0.02$. Conversely, only 19.1% of the disturbed forests experienced net gains ($f_{net}>0.02$), with these forests located mostly in Europe, western Russia, and southern Brazil. The remaining 23.5% of the disturbed forests showed no significant net changes in tree cover ($f_{net}=0\pm0.02$).

We found that some disturbed forests that experienced a net tree cover loss were still associated with a biophysical cooling effect (Fig. 1a, b), especially in tropical and temperate forest grid cells that experienced large fractions of gross tree cover gain, such as in the eastern United States ($f_{net}=-0.01\pm0.004$, $f_{gain}=0.13\pm0.003$, daily mean $\Delta LST_{fc}=-0.05\pm0.002^\circ C$), eastern Congo ($f_{net}=-0.05\pm0.003$, $f_{gain}=0.06\pm0.002$, daily mean $\Delta LST_{fc}=-0.03\pm0.009^\circ C$), and subtropical southern China ($f_{net}=-0.02\pm0.003$, $f_{gain}=0.06\pm0.003$, daily mean $\Delta LST_{fc}=-0.04\pm0.006^\circ C$) (Fig. 1e, f). This result is different from previous findings where deforestation was systematically associated with significant warming$^{5,9,22,23}$. This phenomenon occurred because for the same value of $f_{net}$, the sign and magnitude of $\Delta LST_{fc}$ depended significantly on the absolute values of $f_{gain}$ (Fig. S1). Therefore, we quantified the sensitivity of $\Delta LST_{fc}$ to a unit of gross tree cover gain ($S_{gain}$) and loss ($S_{loss}$) (see Methods). For the global average, $S_{gain}$ ($-0.81\pm0.024^\circ C$) (Fig. 1c) was greater in absolute value than $S_{loss}$ (0.66±0.021°C) (Fig. 1d). The difference in magnitude between $S_{gain}$ and $S_{loss}$ was typically negative in temperate and tropical zones, for instance, in the eastern United States ($S_{gain}=-0.76\pm0.037^\circ C$; $S_{loss}=0.59\pm0.027^\circ C$), eastern Congo ($S_{gain}=-0.80\pm0.091^\circ C$; $S_{loss}=0.53\pm0.095^\circ C$) and subtropical southern China ($S_{gain}=-0.98\pm0.098^\circ C$; $S_{loss}=0.70\pm0.102^\circ C$) (Fig. 1c, d, g, h).

144
<figure><img src="image_4.png" /></figure>

**Fig. 1** Global patterns of tree cover changes in disturbed forests during the 2000-2012 time period and their direct biophysical effects on daily mean land surface temperature (ΔLST<sub>fc</sub>). (a) Net fractional change in tree cover (f<sub>net</sub>) over 0.05° disturbed grid cells that remained forests from 2000 to 2012, (b) daily mean ΔLST<sub>fc</sub> induced by tree cover gains and losses in disturbed forests, (c) sensitivity (S<sub>gain</sub>) of the daily mean ΔLST<sub>fc</sub> to gross tree cover gain (f<sub>gain</sub>), and (d) sensitivity (S<sub>loss</sub>) of the daily mean ΔLST<sub>fc</sub> to gross tree cover loss (f<sub>loss</sub>). The dots in panels **a-d** are spaced at 2° for both latitude and longitude. The right-hand panels **e-h** show zonal mean values of changes in tree cover fractions, ΔLST<sub>fc</sub>, S<sub>gain</sub> and S<sub>loss</sub> averaged into 2° latitude bins, respectively. The shading represents one standard error.
158 The spatial variations in ΔLST<sub>fc</sub> and its distinct sensitivity to f<sub>gain</sub> and f<sub>loss</sub> shown
159 in Fig. 1 raise two questions: (1) What are the values of ΔLST<sub>fc</sub> in disturbed forests
160 with net-zero change in tree cover (f<sub>gain</sub>=f<sub>loss</sub>)? and, (2) what are the values of f<sub>gain</sub> versus
161 f<sub>loss</sub> in disturbed forests that result in net-zero change in LST (ΔLST<sub>fc</sub>=0±0.02°C), that
162 is, the LST anomaly induced by the direct biophysical effect of gross tree cover loss is
163 offset by that induced by gross tree cover gain (hereinafter referred to as LST neutrality)?
164 Answering the first question involves quantifying how disturbed forests with a stable
165 tree cover fraction composed of gross tree cover gain and loss in equal magnitude
166 impact local LST. Answering to the second question provides a benchmark of f<sub>gain</sub>
167 versus f<sub>loss</sub> against which sub-grid tree cover changes in forest grid cells could be
168 managed to achieve a net direct biophysical LST cooling.

169 The plots in Fig. 2 show the variations in ΔLST<sub>fc</sub> in disturbed forest grid cells
170 with all combinations of f<sub>gain</sub> and f<sub>loss</sub>. In tropical and temperate forests, daytime ΔLST<sub>fc</sub>
171 was more negative (cooling) with increasing f<sub>gain</sub> and more positive (warming) with
172 increasing f<sub>loss</sub> (Fig. 2a, b). Nighttime ΔLST<sub>fc</sub> generally responded in the opposite
173 manner (Fig. 2d, e) but with a smaller absolute value than daytime ΔLST<sub>fc</sub>.
174 Consequently, in tropical and temperate forests, the daily mean ΔLST<sub>fc</sub> (Fig. 2g, h)
175 was dominated by daytime ΔLST<sub>fc</sub> signal. In boreal forests, however, the daily mean
176 ΔLST<sub>fc</sub> (Fig. 2i) largely depended on nighttime ΔLST<sub>fc</sub> (Fig. 2f) which had a greater
177 magnitude than daytime ΔLST<sub>fc</sub> (Fig. 2c). The daily mean ΔLST<sub>fc</sub> in this biome was
178 more positive (warming) with increasing f<sub>gain</sub> and more negative (cooling) with
179 increasing f<sub>loss</sub> (Fig. 2i).

180
<figure><img src="image_5.png" /></figure>

Fig. 2 Changes in daytime (a-c), nighttime (d-f), and daily mean (g-i) land surface temperature (ΔLST<sub>fc</sub>) induced by direct biophysical effects of tree cover changes with various fractions of tree cover gain (f<sub>gain</sub>) and loss (f<sub>loss</sub>) in different climate zones from 2000 to 2012. Plots j-l show the average ΔLST<sub>fc</sub> in disturbed forest grid cells with equivalent f<sub>gain</sub> and f<sub>loss</sub> (i.e., f<sub>net</sub>=0±0.02). Each cell in the bubble matrix shows the average ΔLST<sub>fc</sub> observed for a given combination of f<sub>gain</sub> and f<sub>loss</sub> within 0.05° grid cells from 2000 to 2012 reported on the X- and Y-axes in the 0.02 bin, respectively. Red denotes a warming effect (ΔLST<sub>fc</sub>>0.02°C), blue denotes cooling (ΔLST<sub>fc</sub><-0.02°C), and grey represents LST neutrality (ΔLST<sub>fc</sub>=0.0±0.02°C) induced by direct biophysical effects of tree cover gains and losses. The size of the dot indicates the degree of one standard error. The black dashed 1:1 lines represent equal fractions of tree cover gain and loss (i.e., f<sub>gain</sub>=f<sub>loss</sub>). The black solid curves, named the LST-neutral curves, are fitted by quadratic models based on the scatter between f<sub>gain</sub> and f<sub>loss</sub> for grid cells with ΔLST<sub>fc</sub>=0.0±0.02°C (see Methods), thereby separating the biophysical cooling and warming effects on LST. The shading represents the 95% confidence interval assessed by bootstrapping across each grid cell (n=500). The significance (p value) of all fitted curves is less than 0.001. Error bars are given as two
199 standard errors.

200

201 **Fig. 2** shows that warming and cooling on LST induced by direct biophysical
202 effects of tree cover changes were not symmetrically distributed along the 1:1 diagonal
203 line defining $f_{gain}=floss$. The relationship between $f_{gain}$ and $floss$ for disturbed forest grid
204 cells corresponding to LST neutrality ($\Delta LST_{fc}=0.0\pm0.02^\circ C$) can be approximated using
205 a quadratic function, i.e., $f_{gain}=q(floss)$ (see **Methods**), which is shown by the black solid
206 curves in **Fig. 2** (hereinafter referred to as the LST-neutral curves). In tropical and
207 temperate forests, this quadratic function laid below the 1:1 diagonal line, indicating a
208 negative asymmetry of $f_{gain}$ on biophysical LST neutrality (**Fig. 2a, b, d, e, g, h**).
209 Consequently, the grid cells with $f_{gain}=floss$ were associated with a significant daytime
210 cooling effect (tropical: -0.58±0.009°C, temperate: -0.33±0.004°C) and a small
211 nighttime warming effect (tropical: 0.07±0.005°C, temperate: 0.05±0.002°C), leading
212 to an overall cooling effect of the daily mean LST (tropical: -0.25±0.007°C, temperate:
213 -0.14±0.003°C) (**Fig. 2j, k**). Conversely, in boreal forests, the $q$ function was above the
214 1:1 line, implying a positive asymmetry of $f_{gain}$ on biophysical LST neutrality (**Fig. 2c,
215 f, i**). Grid cells with $f_{gain}=floss$ showed a warming effect on daytime LST (0.07±0.002°C)
216 and a stronger cooling effect on nighttime LST (-0.11±0.002°C), resulting in a slight
217 cooling effect on the daily mean LST (-0.02±0.002°C) (**Fig. 2j**). Additionally, these
218 asymmetric responses of $\Delta LST_{fc}$ with respect to $f_{gain}$ versus $floss$ were still robust to the
219 choice of another time period (2003-2012, 2006-2012 and 2009-2012), for larger LST
220 grid-cell sizes (0.1° instead of 0.05°) and under different thresholds of final tree cover
221 for estimating $f_{gain}$ in disturbed forests (**Figs. S2-6**) (see **Methods** for details).
222

223 To quantify the asymmetry influences of $f_{gain}$ versus $floss$ on LST, we calculated the
224 ratio of $f_{gain}$ to $floss$, hereinafter referred to as $\rho$, in disturbed grid cells with biophysical
225 LST neutrality (daily mean $\Delta LST_{fc}=0.0\pm0.02^\circ C$). Over the globe, $\rho$ showed a
226 distinctive latitudinal gradient (**Fig. 3** and **Fig. S7**). In tropical forests, $\rho$ had the
227 smallest values, i.e., below 1.0 (average $\rho=0.75\pm0.025$) (**Fig. 3a**), suggesting that
228 lower gains in tree cover than losses can achieve LST neutrality for this biome. Within
229 tropical forest regions, $\rho$ was smaller in tropical Africa than in tropical South America
and tropical Asia. In temperate forests, the value of ρ was larger than that of tropical forests (average ρ=0.83±0.051) (Fig. 3a) and increased significantly with latitude (Fig. 3b). In boreal forests, ρ mostly varied between 1.0 and 2.0 (average ρ=1.51±0.224) and was the highest in Siberia (Fig. 3a). In this biome, if only the direct biophysical effect was considered, the ratio of $f_{gain}$ to $f_{loss}$ must be smaller than ρ to achieve a negative LST anomaly.

<figure><img src="image_6.png" /></figure>

**Fig. 3** Ratio of $f_{gain}$ to $f_{loss}$ (ρ) in disturbed forest grid cells with land surface temperature neutrality (daily mean ΔLST$_{fc}$ =0.0±0.02°C) induced by direct biophysical effects of tree cover gains and losses in different climate regions (a) and at different latitudes (b) around the globe. The inset histograms in plot a represent mean ρ values in the tropical, temperate and boreal climate zones, respectively. The light green, light blue and light brown colours on the global map indicate the tropical, temperate and boreal climate regions, respectively. The floating numbers on the map represent the average ρ values for the corresponding regions. Error bars are given as one standard error. The curve in plot b indicates the median values of ρ in 5° latitude bins. The shading represents one standard error.

### Mechanisms of the asymmetrical impact on ΔLST$_{fc}$

The asymmetric responses of ΔLST$_{fc}$ with respect to $f_{gain}$ and $f_{loss}$ can be explained by the asymmetric influences of tree cover gain versus loss on the surface energy balance<sup>7,40-44</sup> (Fig. 4a-1 and Fig. S8), which were diagnosed using satellite
observations of albedo, shortwave downwelling radiation (SW) and latent heat (LE)
turbulent fluxes (see Methods for details).

In the tropical and temperate forests, the neutral curves for changes in the surface
energy fluxes were all below the 1:1 diagonal line in the [f<sub>gain</sub>, f<sub>loss</sub>] spaces (Fig. 4a, b,
d, e, g, h, j, k). Disturbed tropical forests with f<sub>gain</sub>=f<sub>loss</sub> showed lower values of
reflected SW (albedo multiplied by incoming SW) (ΔSW=-0.9±0.2W/m<sup>2</sup>) (Fig. 4m),
higher values of LE (ΔLE=5.1±0.4W/m<sup>2</sup>) (Fig. 4n), and small increases in sensible
heat and ground heat fluxes (Δ(H + G)=0.2±0.1W/m<sup>2</sup>) (Fig. 4o) compared with
undisturbed forests. Overall, these processes caused a significant net decrease in surface
energy budget (ΔLW=-4.4±0.2W/m<sup>2</sup>) (Fig. 4p), which explained the cooling signal
shown in Fig. 2. In temperate forests, the grid cells with f<sub>gain</sub>=f<sub>loss</sub> showed a moderate
cooling effect, indicated by a moderate reduction in SW reflection (ΔSW =-
0.5±0.1W/m<sup>2</sup>) (Fig. 4m), a moderate increase in LE (ΔLE=2.0±0.2W/m<sup>2</sup>) and H and G
fluxes (Δ(H + G)=0.4±0.1W/m<sup>2</sup>) (Fig. 4o). In these two biomes, the stronger increase
in evapotranspiration (ET) associated with tree cover gain compared to the decrease
from tree cover loss was the main cause of the negative asymmetry of f<sub>gain</sub> on ΔLST<sub>fc</sub>
in the disturbed forests, where tree cover losses were mainly induced by commodity-
driven deforestation, shifting agriculture, and forestry<sup>40</sup> (inset histograms in Fig. 4a, b,
d, e, g, h, j, k; Figs. S9-S14). This is because the young trees associated with tree cover
gain are usually shorter and have higher leaf water potentials and a consequent larger
ET than the previous tree cover<sup>45-49</sup>. In contrast, the changes in reflected SW induced
by albedo differences were negligible between newly grown young trees and previously
lost older trees<sup>42,43</sup>. These processes eventually led to a negative asymmetric influence
of tree cover gain versus loss on ΔLST<sub>fc</sub> (Fig. 2). By matching GFW data<sup>15</sup> with
planting years from a new global map of plantations<sup>50</sup> (see Methods for details), we
show that the LST-neutral curves for plantations older than 6 years were slightly more
distant from the 1:1 diagonal lines than those of younger plantations (Fig. 5a, b),
indicating that tree age is one vital factor influencing the asymmetry of f<sub>gain</sub> versus f<sub>loss</sub>
on ΔLST<sub>fc</sub>. A space-for-time analysis over a longer period indicated that direct
biophysical cooling of plantations on LST diminished as planted trees became older
than approximately 28 years in tropical regions and 32 years in temperate regions (Fig. 5c). This result implies that influences of tree age on asymmetric patterns of ΔLST<sub>fc</sub> may become weaker when trees exhibit lower growth rates<sup>51</sup> (see Methods).

In boreal forests, the situation is different. The neutral curves in the [f<sub>gain</sub>, f<sub>loss</sub>] spaces were above the 1:1 diagonal line for ΔSW and Δ(H + G) and below the 1:1 diagonal line for ΔLE (Fig. 4c, f, i). Disturbed grid cells in which f<sub>gain</sub>=f<sub>loss</sub> were associated with a large increase in reflected SW (ΔSW=1.8±0.3W/m²) (Fig. 4m), a small increase in ET (ΔLE=0.5±0.2W/m²) (Fig. 4n) and a strong decrease in H and G (Δ(H + G) =-1.3±0.2W/m²) (Fig. 4o). These processes resulted in a minor decrease in the surface energy budget (ΔLW=-1.0±0.1W/m²) (Fig. 4l, p) and thus a negligible biophysical cooling effect (Fig. 2). Therefore, the change in SW was the main cause for the asymmetric changes in the surface energy balance caused by tree cover gain versus loss whereas tree age-induced ET changes played a less important role. The stronger contribution of SW changes in boreal forests was mainly attributed to the lower forest albedo during snow-covered periods<sup>6,52-54</sup>, typically 20% to 50% less than in snow-covered open areas. In addition, the dominant coniferous forests in boreal region<sup>13</sup> were typically darker (lower albedo)<sup>6</sup> than broadleaved trees prevailing elsewhere<sup>55,56</sup>. Forestry and wildfire were the two dominant causes of tree cover losses in disturbed boreal forests (inset histogram in Fig. 4c, f, i, l; Figs. S9-14). However, standing dead trees at recently burnt sites only partially masked winter snow cover and led to a weaker albedo increase than from timber harvest<sup>7,18</sup>, which caused a strong increase in the reflected SW for forestry compared to wildfire (Fig. 4c and Fig. S12). Therefore, forestry-induced net changes in SW seem to be the main driver of the asymmetric response of ΔLST<sub>fc</sub> in boreal forests where f<sub>gain</sub>=f<sub>loss</sub>. This finding is consistent with previous studies<sup>5,24,42,57</sup> that showed that albedo-induced net change in SW was the major cause of the change in direct biophysical effects on LST over boreal forests.

To assess the uncertainties of satellite-based retrievals of the surface energy balance, in addition to the MODIS ET dataset<sup>58</sup> used above, we tested two alternative datasets: the 0.05° resolution ET data products from Global Land Surface Satellite (GLASS)<sup>59</sup> and Penman-Monteith-Leuning (PML_v2) ET<sup>60</sup>. These two different ET
datasets (Fig. S18) showed marginally small differences compared with those derived from MODIS ET (Fig. S8d-f), confirming the robustness of the explanations for the observed asymmetry patterns of $\Delta LST_{fc}$.

<figure><img src="image_7.png" /></figure>

Fig. 4 Changes in surface energy balance components (reflected shortwave radiation from the surface [$\Delta SW$, a-c], latent heat [$\Delta LE$, d-f], sensible heat and ground heat fluxes [$\Delta(H + G)$, g-i], and emitted longwave radiation from the surface [$\Delta LW$, j-l]) induced by tree cover changes with various fractions of tree cover gain ($f_{gain}$) and loss ($f_{loss}$) in different climate zones from 2000 to 2012. $\Delta$ stands for the difference between disturbed and undisturbed forests. Each cell in the bubble matrix shows the average value of each energy flux component for a given combination of $f_{loss}$ and $f_{gain}$ in 0.05° pixels on the X- and Y-axes in the 0.02 bin, respectively. Red indicates a positive value, blue indicates a negative value, and grey indicates a value of zero. The size of the dot indicates the degree of one standard error.
The black dashed 1:1 diagonal lines represent equal values of tree cover gain and loss (i.e., $f_{gain} = f_{loss}$). The neutral curves are fitted by quadratic models based on the scatters between $f_{gain}$ and $f_{loss}$, similar to the LST neutral curves in Fig. 2. The shading represents the 95% confidence interval assessed by bootstrapping across each pixel (n=500). The significance (*p* value) of all fitted curves is less than 0.001. The average anomaly of each component in the surface energy balance induced by tree cover changes with equivalent $f_{gain}$ and $f_{loss}$ (i.e., $f_{net}=0\pm0.02$) is shown in plots **m-p**, while their further details for forest grid cells disturbed by different drivers are graphed as inset histograms in plots **a-l**. C, S, F and W denote commodity-driven deforestation, shifting agriculture, forestry and wildfire, respectively. Error bars are given as one standard error.

<figure><img src="image_8.png" /><figcaption>Fig. 5 Influences of tree age on the asymmetric pattern of $\Delta LST_{fc}$ induced by direct biophysical effects of tree cover gain and loss in tropical and temperate forests. (a-b) LST-neutral curves for disturbed forest grid cells with different planting ages (black: planting age$\le6$ years; red: $6$ years$<\text{planting age}\le12$ years) in tropical (a) and temperate (b) zones. The LST-neutral curves are fitted by quadratic models (see Figs. S15 and S16 for details). (c) Variations in the daily mean $\Delta LST_{fc}$ along with tree age (bin times: 1-year) in planted forests. Tree age influenced $\Delta LST_{fc}$ in planted forests with different tree covers (i.e., $50\% < f_{gain} \le 70\%$ versus $f_{gain} > 70\%$), as shown in Fig. S17. The shading represents the 95% confidence interval, assessed by bootstrapping across each pixel (n=500). The significance (*p* value) of all fitted curves is less than 0.001.</figcaption></figure>
353 By comparing with the tree cover data recorded by the Food and Agriculture
354 Organization of the United Nations (FAO), a standard reference for global-scale forest
355 resource information produced at decadal intervals, previous studies indicated that
356 significant uncertainties exist in some regions (such as Canada, China, Brazil) in the
357 GFW tree cover data<sup>61-63</sup>. This is partially due to that GFW tree cover data could reflect
358 tree cover changes caused by not only harvest activities but also natural disturbances in
359 unmanaged forests which were not in the scope of field forest inventories<sup>64</sup>. To address
360 this issue, here we used alternative tree cover maps from individual countries or regions
361 including Canada<sup>65</sup>, USA<sup>66</sup>, eastern Europe<sup>67</sup>, northern Europe<sup>68</sup>, China<sup>69</sup>, and tropical
362 moist forests<sup>70</sup> (Table S2 and Fig. S19), which were calibrated or validated using
363 national forest cover statistics or field inventory data (Fig. S20) and were assumed to
364 be more accurate than GFW tree cover data<sup>71</sup>, as a means to provide an alternative data
365 source for tree cover gain and loss<sup>72</sup>.

366 Generally, the distributions of LST-neutral curves in the [f<sub>gain</sub>, f<sub>loss</sub>] spaces, based
367 on the five regional tree cover datasets, were always on the same side of the 1:1 diagonal
368 line as those generated by corresponding GFW tree cover data (Figs. S21-26),
369 indicating the robustness of our findings on the asymmetric response of ΔLST<sub>fc</sub> to f<sub>gain</sub>
370 versus f<sub>loss</sub> at the global scale. In temperate forests, for example in the USA, both f<sub>gain</sub>
371 and f<sub>loss</sub> agreed well with those from the National Land Cover Database (NLCD)<sup>66</sup> (Fig.
372 S21), and the ρ difference (Δρ) for the LST-neutral curves between NLCD and GFW
373 tree cover data was approximately equal to zero. However, an underestimation of tree
374 cover gains often occurred in regions with large afforestation programs such as China
375 <sup>22</sup>, the largest tree-planting country in the world. Thus, the negative asymmetry of f<sub>gain</sub>
376 on LST neutrality in China from GFW data was significantly weakened
377 (Δρ=0.07±0.019), but the LST-neutral curves from regional data were still below the
378 1:1 diagonal line (Fig. S22). In tropical moist forests, the lower performance of GFW
379 tree cover data mainly stemmed from an overestimation of tree cover compared with
an underestimation in dry tropical forests<sup>73-75</sup>. Our analyses showed that 68% of disturbed pixels in GFW tree cover data had a lower value of *f*<sub>loss</sub>, ten percent smaller than the regional data from Joint Research Centre<sup>70</sup>. The negative asymmetry of *f*<sub>gain</sub> on LST neutrality for this biome was to some extent underestimated in GFW tree cover data (Δρ=-0.07±0.008) (Fig. S23).

The situations differ in the boreal forests of eastern Europe, northern Europe, and Canada. In eastern Europe, *f*<sub>gain</sub> and *f*<sub>loss</sub> of GFW tree cover data were almost equally underestimated compared with a more accurate regional data from the Global Land Analysis and Discovery Laboratory<sup>67</sup>, and the asymmetry patterns of Δ*LST*<sub>fc</sub> changed marginally (Δρ=0.03±0.001) (Fig. S24). However, in countries such as Norway, Finland, and Sweden with low sun angle and often cloudy weather<sup>76</sup> and where much of the nonclear-cutting harvest activities took place in small and irregular areas<sup>77</sup>, GFW tree cover data likely had a lower *f*<sub>loss</sub> compared with regional data from the Copernicus Land Monitoring Service<sup>78,79</sup> (Figs. S25 a-c). In contrast, in the boreal region of Canada with widespread low-density tree communities<sup>80</sup> and significant wildfire losses in less productive forests that require long recovery times<sup>81</sup>, GFW tree cover data likely underestimated *f*<sub>gain</sub> compared with data from the National Terrestrial Ecosystem Monitoring System<sup>65</sup> (Fig. S26 a-c). Results based on these regional datasets indicated a weaker positive asymmetry of *f*<sub>gain</sub> on LST neutrality in northern Europe (Δρ=-0.12 ±0.010) but a stronger one in Canada (Δρ=0.07±0.023) (plots d-i in Fig. S25-26).

Overall, the asymmetric patterns of Δ*LST*<sub>fc</sub> with respect to the newly calibrated *f*<sub>gain</sub> versus *f*<sub>loss</sub> from regional tree cover datasets are consistent with those of GFW.

### Uncertainties caused by the asymmetric patterns of Δ*LST*<sub>fc</sub> in the time-series analysis and space-for-time analogy

Repeated and consistent satellite observations have been widely used to quantify the direct biophysical effects of forest cover changes on LST from a global perspective<sup>5,22,24,41,44</sup>. The most difficult challenge in the time-series analysis has been how to separate direct biophysical effects of tree cover changes from climate
variability<sup>5,36</sup>. To overcome this challenge, Alkama and Cescatti<sup>5</sup> selected forest grid
cells that experienced net-zero changes in forest cover and attributed their LST
anomalies to climate variability. However, herein we highlighted an asymmetric effect
of tree cover gain versus loss on LST induced by their direct biophysical processes so
that forest grid cells with net-zero change in forest cover may lead to either a negative
or positive LST anomaly, depending highly on the background climate and magnitude
of gross tree cover gain and loss with distinct biophysical properties. Therefore, an
optimal reference in the time-series analysis should be selected from undisturbed
forests with no gross tree cover gain and loss to disentangle direct biophysical LST
effects of forest cover changes from climate variability.

The space-for-time analogy is a common approach for estimating the direct
biophysical effects of forest cover changes on LST<sup>13,24</sup>. However, inconsistencies in
both sign and magnitude have been observed between satellite- and ground-based
studies<sup>24,42</sup>. It has been argued that the ground-based, near-surface air
temperature<sup>7,12,23,82,83</sup> was less sensitive to aerodynamic resistance<sup>5,42,44</sup> associated with
forest cover changes than the satellite-based LST (i.e., skin temperature)<sup>5,12,13,24,84</sup>,
leading to the above controversies between satellite- and ground-based studies. The
asymmetric patterns of ΔLST<sub>fc</sub> in response to tree cover gain and loss can provide an
alternative explanation. Satellite-based LST from coarse and moderate sensors usually
sample a land pixel which represents the mixed biophysical LST effects of sub-grid
gross tree cover gains and losses. In contrast, tower studies have a smaller footprint and
sample small forest stands<sup>50,85,86</sup>. However, this mismatch in spatial scales was mostly
neglected in space-for-time methods to infer the biophysical effects of forest cover
change, as this approach simplifies that the temperature differences between paired sites
are explained solely by net differences in forest cover<sup>42</sup>.

434

435 **Limitations and potential implications**

The most significant limitation is the accuracy and consistency of satellite signals
in capturing fine-scale tree cover changes globally. While many satellite tree cover data
are regionally validated, it is important to recognize that a comprehensive validation
using field inventory tree cover data remains challenging<sup>63</sup>, and in most cases is only partially implemented<sup>64</sup>. One important discrepancy between satellite-based and field inventory tree cover data is likely driven by forest management activities (e.g., harvest activity and selective thinning) which results in changes in tree cover, but not land cover change<sup>71</sup>. Thus, global tree cover datasets fully validated by field inventory tree cover data are necessary for accurately quantifying the direct biophysical effects of global forests in the future.

Second, planting or regenerating trees in previously disturbed forests to restore their biophysical cooling are one of the important natural climate solutions to mitigate local surface warming<sup>46</sup>. Nevertheless, the direct biophysical temperature effect analyzed in this study is different from the full climate impacts, involving both indirect biophysical<sup>87,88</sup> and biogeochemical effects<sup>89,90</sup>. Indirect biophysical effects tend to enhance clouds over temperate and boreal forests but inhibit cloud formation over tropical/subtropical forests<sup>14</sup>. Thus, ongoing fine-scale tree cover changes could either dampen or amplify surface temperature change through their indirect biophysical<sup>88,91-94</sup> and biogeochemical effects<sup>95</sup>. There is still a long way to go towards fully accounting for biogeochemical and direct/indirect biophysical changes<sup>11,21,30,96,97</sup> of fine-scale forest management in climate mitigation policies like the Paris Agreement<sup>5,30,98</sup>.

In conclusion, our analysis provides a first global estimate of direct biophysical effects of gross tree cover gain and loss at fine resolution. It demonstrates an asymmetric effect of tree cover gain versus loss on LST. This result helps quantifying an average ratio of tree cover gain to loss to achieve a net direct biophysical cooling, which could be considered for climate-smart forest management<sup>99</sup>. Moreover, the asymmetric patterns of direct biophysical effects on LST may also result in various cascading effects on biodiversity (e.g., species distribution)<sup>100</sup>, functional traits (e.g., leaf phenology)<sup>101,102</sup>, and ecosystem functioning (e.g., photosynthesis)<sup>103</sup>, which are strongly driven by local temperatures<sup>100</sup>.

466

## Methods

### *Calculating fractions of gross tree cover gains and losses*
As an initial step, we overlaid the 30-m high-resolution global tree cover change maps from Global Forest Watch (GFW)¹⁵ onto 0.05° resolution MODIS MCD12C1 land cover images³⁹, and selected the grid cells that were defined as “forestlands” in the MODIS land cover images. The 0.05° resolution forest grid cells that had experienced fractional gains ($f_{gain}>0.02$) or losses ($f_{loss}>0.02$) in tree cover from 2000 to 2012 based on GFW tree cover maps were considered as disturbed forests (stand-replacing natural disturbance, forest management, changes in canopy density etc.). The $f_{gain}$ and $f_{loss}$ denote the fractions of tree cover gain and tree cover loss for the 0.05° grid cell, respectively.

GFW maps recorded three types of pixels, i.e., tree cover gain, loss, and both¹⁵, while only had the tree cover fraction information for 30-m resolution pixels in 2000. Here, we estimated the fractions of tree cover gain and loss for the 0.05° disturbed forest grid cell in 2012 by taking 2000 as the benchmark year. Overall, three cases were considered: (i) In a 0.05° resolution forest grid cell $k$, for pixel $i$ labelled only “forest loss” in GFW 30-m resolution maps, the tree cover decreased from the fraction ($f_i^{30m}$) in 2000 to zero by 2012; this is a direct estimate of $f_{loss}$ using GFW tree cover data. (ii) For pixel $j$ labelled only “forest gain”, we assumed that the tree cover would increase to the average values ($f_{ref,j}^{30m}$) of neighboring well-grown forests (surrounding 9×9 pixels) with tree cover fractions higher than 50%. To verify this assumption, we compared the tree cover fraction of a forest planted from 1982 to 2000⁵⁰ to the tree cover fractions of well-grown forests (9×9 pixels) surrounding that planted forest. The results show that the fractions were strongly and linearly correlated along the 1:1 diagonal line (all R² values >0.98, Fig. S27). (iii) For pixel $z$ with both tree cover gains and losses, we assumed that the tree cover would initially decrease from the fraction ($f_z^{30m}$) in 2000 to zero, as did the pixels labelled only “forest loss”. Then, we assumed that the tree cover fractions would increase to the average fraction ($f_{ref,z}^{30m}$) of surrounding well-grown forests in 2012, similar to pixels labelled only “forest gain”. Thus, the average fraction of tree cover losses ($f_{loss,k}^{0.05°}$) and gains ($f_{gain,k}^{0.05°}$) for disturbed forest grid cell $k$ was calculated using **Equations 1 and 2**, respectively.
$$f_{loss,k}^{0.05^{\circ}} = [\sum_{1}^{i}(f_{i}^{30m}) + \sum_{1}^{z}(f_{z}^{30m})] \times \frac{0.00025 \times 0.00025}{0.05 \times 0.05} \quad (1)$$

$$f_{gain,k}^{0.05^{\circ}} = [\sum_{1}^{j}(f_{ref,j}^{30m} - f_{j}^{30m}) + \sum_{1}^{z}(f_{ref,z}^{30m})] \times \frac{0.00025 \times 0.00025}{0.05 \times 0.05} \quad (2)$$

where $f_{gain,k}^{0.05^{\circ}}$ and $f_{loss,k}^{0.05^{\circ}}$ denote the fractions of tree cover gains and tree cover losses for the 0.05°disturbed forest grid cell $k$, respectively. $f_{i}^{30m}$ represents the tree cover fraction of the 30-m (~0.0025°) resolution grid $i$ within the 0.05° forest grid $k$ in 2000. $f_{ref}^{30m}$ represents the average tree cover fraction of surrounding 9×9 30-m resolution pixels with tree cover fractions higher than 50% around the target pixel in 2000. $i$ and $j$ denote the number of 30-m resolution pixels labelled as only forest gains and losses, respectively. $z$ denotes the number of 30-m resolution pixels that experienced both forest gains and losses. $\frac{0.00025 \times 0.00025}{0.05 \times 0.05}$ is the conversion coefficient, which is the ratio of the spatial resolution in GFW tree cover maps to that of the MODIS MCD12C1 land cover maps. We then selected the 0.05° grid cells with tree cover gain ($f_{gain}>0.02$) or loss fractions ($f_{loss}>0.02$) during the study period (2000-2012) in GFW tree cover change datasets as the disturbed forest grid cell for the analyses.

### Calculating ΔLST<sub>fc</sub> caused by tree cover gains and losses

We used the 0.05° resolution MOD11C3 v061 daytime and nighttime LST products³⁸ (refers to the skin temperature of land surfaces⁵,⁷,³⁸,⁴²) to represent the daytime and nighttime LST, respectively, and calculated the daily mean LST by averaging the MODIS daytime and nighttime LST. As the temperature anomaly ($\Delta LST_{total}$) between two years in a given disturbed forest grid cell was the combined effect induced by both tree cover change and climate variability⁵, we used the time-series analysis methodology developed by Alkama and Cescatti⁵ (Equation 3) to disentangle the direct biophysical effect of tree fractional gain and loss ($\Delta LST_{fc}$) from that due to climate variability ($\Delta LST_{cv}$).

In the method by Alkama and Cescatti⁵, forest grid cells with $f_{net}=0 \pm 0.02$ were defined as reference undisturbed forests for estimating $\Delta LST_{cv}$. In our methods, instead, more stringent criteria were used to constrain the undisturbed grid cells: (1) to eliminate
impacts from tree cover changes, both $f_{gain}$ and $f_{loss}$ in undisturbed forest grid cells should be smaller than 0.02 over 2000-2012; and (2) to eliminate impacts from forest degradation, undisturbed grid cells should also have small variations in the Normalized Difference Vegetation Index (NDVI) ($\Delta NDVI=0\pm0.02$) during the same period. For a certain disturbed forest grid cell, its reference grid cells were detected from neighboring undisturbed forests located within a distance of 50km$^5$, as grid cells within 50 km were assumed to share the most similar climate background$^{24}$. We then took the temperature anomalies of reference undisturbed grid cells between 2000 and 2012 as those induced by climate variability without the interference of tree cover changes (i.e., $\Delta LST_{fc} \approx 0$ and $\Delta LST_{total} = \Delta LST_{cv}$). To limit the influence caused by distance, all $\Delta LST_{cv}$ values within 50 km of the disturbed forest grid cells were averaged using the inverse distance as a weighting factor, as shown in Equation 4$^5$,

$$ \Delta LST_{fc} = \Delta LST_{total} - \Delta LST_{cv} \qquad (3) $$

$$ \Delta LST_{cv} = \frac{\sum_{k=1}^{n} \frac{\Delta LST_k}{d_k}}{\sum_{k=1}^{n} \frac{1}{d_k}} \qquad (4) $$

where $\Delta LST_{total}$ (°C) signifies the overall LST change in disturbed forest grid cells; $\Delta LST_{fc}$ (°C) denotes the LST change in disturbed forest grid cells caused by tree cover gains and losses; $\Delta LST_{cv}$ (°C) is the LST change induced by climate variability; $\Delta LST_k$ (°C) denotes the LST changes in reference undisturbed forest grid cells (k) and $d_k$ is the distance between the disturbed and undisturbed forest grid cells (k) in km.

We further quantified the sensitivity of $\Delta LST_{fc}$ to the fraction of tree cover gain ($f_{gain}$) and tree cover loss ($f_{loss}$) with a linear regression model as follows:

$$ \Delta LST_{fc} = S_{gain} \times f_{gain} + S_{loss} \times f_{loss} \qquad (5) $$

where $S_{gain}$ (°C) and $S_{loss}$ (°C) express the sensitivities of $\Delta LST_{fc}$ to $f_{gain}$ and $f_{loss}$, respectively. Here, grid cells were analyzed using a moving window of 6×6°, shifted by 2° at each step, as shown in Fig. 1c, d.

Detecting asymmetric patterns of $\Delta LST_{fc}$ and temperature-neutral curves induced by tree cover gains versus losses
Bubble matrix plots were used to show asymmetric responses of ΔLST<sub>fc</sub> with respect to f<sub>gain</sub> versus f<sub>loss</sub>. As shown in Fig. S28, in the bubble matrix, the colour of each cell represents the average value of ΔLST<sub>fc</sub> observed for a given combination of f<sub>gain</sub> and f<sub>loss</sub> within the 0.05° grid cell. Red denotes a warming effect (ΔLST<sub>fc</sub>>0.02°C), blue indicates cooling (ΔLST<sub>fc</sub><-0.02°C) and grey represents a net-zero temperature change (ΔLST<sub>fc</sub>=0.0±0.02°C) between 2000 and 2012. The X- and Y- axes representing the f<sub>loss</sub> and f<sub>gain</sub>, respectively, were plotted schematically in the 0.2 bin in Fig. S28 and the 0.02 bin in Fig. 2.

The LST-neutral curve was defined as the boundary between dots with negative ΔLST<sub>fc</sub> (cooling) and dots with positive ΔLST<sub>fc</sub> (warming) in the [f<sub>gain</sub>, f<sub>loss</sub>] space. To simulate the LST-neutral curve, we selected the dots with ΔLST<sub>fc</sub>=0.0±0.02°C in the bubble matrix plots (Fig. 2). We then examined multiple linear and nonlinear (e.g., quadratic, cubic, general additive models) regressions to fit the relationships between f<sub>gain</sub> and f<sub>loss</sub> and used the Akaike Information Criterion (AIC) to select the optimal model<sup>104</sup>. Finally, a quadratic function was chosen as the best model to regress the nonlinear relationship between f<sub>gain</sub> and f<sub>loss</sub> where ΔLST<sub>fc</sub>= 0.0±0.02°C: f<sub>gain</sub> = q(f<sub>loss</sub>), as depicted by the black solid curves in Fig. 2.

Assessing the surface energy balance to interpret the asymmetric patterns of ΔLST<sub>fc</sub>

Net solar radiation received on the ground is converted into sensible heat flux (H), latent heat flux (LE), and ground heat flux(G). The function of the surface energy balance is expressed as follows<sup>13</sup>:

$$SW_{\downarrow} - SW_{\uparrow} + LW_{\downarrow} - LW_{\uparrow} = LE + H + G \quad (6)$$

where SW<sub>↓</sub> and SW<sub>↑</sub> denote the downwelling shortwave radiative flux incident on the ground (total solar radiation) and reflected solar shortwave radiation from the surface (reflected shortwave radiation), respectively. LW<sub>↓</sub> and LW<sub>↑</sub> denote the longwave radiation from the atmosphere (atmospheric downward radiation) and longwave radiation emitted from the surface to the atmosphere (surface emitted radiation), respectively. LE is the latent heat flux referring to the transfer of heat due
to the transitional phase of water in the atmosphere. H is the sensible heat flux referring to the heat transfer between the ground and air caused by the turbulent movement of the surface layer. G is the ground heat flux representing the quantity of energy transfer between the surface and deep soil.

Herein, we used the method of Duveiller and colleagues¹³ to assess the potential impact of tree cover changes on the surface energy balance, which assumed that the tree cover change at 30-m resolution is not strong enough to induce cloud feedback and the assigned net-zero change in $SW_↓$ and $LW_↓$ (i.e., $\Delta SW_↓ = 0$ and $\Delta LW_↓ = 0$)¹⁰⁵. The change in residual fluxes, composed of both H and G, can be estimated by **Equation 7**. Steps for the derivation of **Equation 7** are shown in the Supplementary Information.

$$ \Delta(H + G)_{fc} = -\Delta SW_{\uparrow,fc} - \Delta LW_{\uparrow,fc} - \Delta LE_{fc} \quad (7) $$

where $\Delta$ refers to the changes in the components of the surface energy balance; the subscript fc represents changes in energy fluxes induced by tree cover changes; $\Delta LE_{fc}$ was calculated by removing the average $\Delta LE$ of reference undisturbed grid cells from that of corresponding disturbed forest grid cells.

The changes in the reflected shortwave radiation ($\Delta SW_{\uparrow,fc}$) in response to tree cover changes can be expressed as the product of changes in albedo ($\Delta albedo$) and shortwave downwelling radiative fluxes ($SW_↓$), shown in **Equation 8**.

$$ \Delta SW_{\uparrow,fc} = \Delta albedo \times SW_↓ \quad (8) $$

The changes in $LW_{\uparrow,fc}$ (referred to as $\Delta LW_{\uparrow,fc}$) in response to tree cover change can be physically derived using **Equation 9**²³:

$$ \Delta LW_{\uparrow,fc} = \epsilon \Delta LST_{fc} 4\sigma LST^3 \quad (9) $$

where $\epsilon$ is broadband emissivity and $\sigma$ represents the Stefan-Boltzmann constant ($\sigma=5.67 \times 10^{-8} \text{ W/(m}^2\cdot\text{K}^4)$). MOD11C3 products³⁸ provide emissivity estimates, where $\epsilon$ can be calculated using an empirical equation¹⁰⁶: $\epsilon = 0.2122\epsilon_{29} + 0.3859\epsilon_{31} + 0.4029\epsilon_{32}$. $\epsilon_{29}$, $\epsilon_{31}$, and $\epsilon_{32}$ represent the estimated emissivity in MODIS bands 29 (8400-8700 nm), 31 (10780-11280 nm) and 32 (11770-12270 nm).

The data sources of the components in surface energy balance are shown in **Table**
S1. The *LE* data for calculating Δ*LE* were derived from the MOD16A3GF v061 products<sup>58</sup>. The *albedo* and *SW*↓ data for calculating Δ*SW*↑,fc were obtained from MCD43C3 v061 products at a 0.05° resolution and from GLASS DSR (V60) data at a 0.05° resolution<sup>59</sup>, respectively. The daytime and nighttime *LST* data were from the MOD11C3 v061 *LST* products<sup>38</sup>, while the daily mean Δ*LST*fc for estimating Δ*LW*↑,fc was calculated as the average of the daytime and nighttime Δ*LST*fc. The Δ(*H* + *G*)fc component was calculated from Δ*SW*↑,fc, Δ*LW*↑,fc and Δ*LE*fc based on Equation 7.

619
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**Evaluating the influences of scale transformation, study period, tree age, disturbance**
621
**driver and tree cover data on the asymmetric responses of ΔLST<sub>fc</sub>**

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To assess potential uncertainties caused by scale transformation<sup>107</sup> that might be
induced by matching 30-m resolution tree cover changes<sup>15</sup> with the 0.05° resolution
MODIS land cover and LST data<sup>39</sup>, we produced additional bubble matrix plots of
ΔLST<sub>fc</sub> at 0.1° resolution (Fig. S6) to compare with those at 0.05° resolution (Fig. 2).
If the LST-neutral curves at the 0.1° and 0.05° resolutions varied significantly, the
asymmetric patterns of ΔLST<sub>fc</sub> were treated as strongly scale-dependent
transformation, or otherwise were considered scale-independent or rarely scale-
dependent. To assess potential uncertainties from our assumptions on f<sub>gain</sub> in GFW tree
cover data, we additionally plotted the asymmetric patterns of ΔLST<sub>fc</sub> against f<sub>gain</sub>
versus f<sub>loss</sub> under different scenarios for pixels with tree cover gain, whose final tree
covers in 2012 were assumed to be 50% (minimum), 75% (moderate) and 100%
(maximum), respectively (Figs. S3-5).

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On GFW maps, the pixels of tree cover loss were recorded separately in each year
from 2000 to 2012, while the pixels of tree cover gain were only given for the whole
period without planting year information¹⁵. To allocate the fractions of tree cover gains
to each year from 2000 to 2012, we overlaid GFW tree cover change map onto a 30-m
resolution global dataset of tree plantations⁵⁰. We subsequently assigned the
information of planting years to corresponding 30-m resolution pixels labelled as tree
cover gain on GFW maps. Considering the inconsistency in coverage between GFW
tree cover maps and the tree plantation maps⁵⁰, only the 0.05° resolution grid cells, with more than 80% of total 30-m resolution pixels being assigned with planting year information, were included in the analysis. After allocating the fractions of tree cover gain yearly, we examined the asymmetric responses of ΔLST<sub>fc</sub> and corresponding neutral curves for various combinations of f<sub>gain</sub> and f<sub>loss</sub> among the different time periods (i.e., 2003-2012, 2006-2012 and 2009-2012) (Fig. S2).

Next, by matching tree age information⁵⁰ with GFW tree cover maps, we quantified the influences of tree age on the asymmetric patterns of ΔLST<sub>fc</sub> and corresponding LST-neutral curves for pixels at 0.05° resolution with different tree planting ages (i.e., ages ≤6 years versus 6 years<ages≤12 years) (Figs. S15 and S16). Additionally, we further used the space-for-time method over a longer period to quantify the impact of tree age on ΔLST<sub>fc</sub> (Fig. 5). By matching the global tree plantation data of Du et al.⁵⁰ with the MODIS LST time-series, we estimated the ΔLST<sub>fc</sub> by using the LST of forest grid cells minus the LST of reference nonforest grid cells within a 50-km radius of the forest grid cell⁵. Only the 0.05° resolution forest grid cells with more than 80% of their sub-grid 30-m resolution forest pixels assigned with information of planting trees based on Du's map were included in the analysis. To eliminate the influences of tree cover, forest grid cells with 50%<f<sub>gain</sub>≤70% (Fig. S17a, c, e) and f<sub>gain</sub>>70% (Fig. S17b, d, f)) were analyzed separately.

Curtis et al.⁴⁰ classified five main drivers of tree cover loss in global forests (10-km resolution): commodity-driven deforestation (permanent conversion from forestland to nonforest land); forestry (large-scale forestry operations within forests), shifting agriculture (conversion from forest to agriculture lands), wildfire (burning of forest vegetation), and urbanization (conversion from forest to urban areas). To investigate the underlying mechanism, we graphed the bubble matrix plots of ΔLST<sub>fc</sub> and changes in energy fluxes against f<sub>gain</sub> and f<sub>loss</sub> for disturbed forest grid cells with different drivers of tree cover loss, respectively (Figs. S9-14).

Finally, we used five regional forest cover datasets in Canada⁶⁵, USA⁶⁶, eastern Europe⁶⁷, northern Europe⁶⁸, China⁶⁹, and the whole tropical region⁷⁰ (Table S2) to test potential uncertainties from tree cover change data. For regional tree cover data, we
classified the 30-m resolution pixels of forest gain and loss as follows: (1) forest pixels
converted from other land cover types are recognized as type of “forest gain”; (2)
nonforest pixels converted from forests are detected as type of “forest loss”; (3) pixels
having ever undergone both processes (1) and (2) are recognized as type of “forest gain
and loss”. Then, we compared *f*<sub>gain</sub> and *f*<sub>loss</sub> in GFW tree cover data with those of the
newly generated tree cover datasets, calibrated by national forest cover statistics or field
forest inventory data (Fig. S20), at 0.05° resolution and tested the asymmetric patterns
of Δ*LST*<sub>*fc*</sub> in response to *f*<sub>gain</sub> versus *f*<sub>loss</sub> (Figs. S21-26).

679

680 Acknowledgments

681 We sincerely thank the editor and anonymous reviewers for their insightful
682 comments and valuable suggestions. We also thank Dr. Nicholas Coops from The
683 University of British Columbia and Dr. Sergey Bartalev from Russian Academy of
684 Sciences for providing the regional tree cover datasets and editing the paper. This study
685 was supported by the National Natural Science Foundation of China (Nos. 41971275,
686 31971458, and U21A6001), the Guangdong Major Project of Basic and Applied Basic
687 Research (No. 2020B0301030004), and Innovation Group Project of Southern Marine
688 Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311021009).
689

699

690 Data Availability

691 The land surface temperature, land cover, evapotranspiration, albedo, forest age,
692 and energy flux data used for the analyses in this study are available online as follows,
693 30-m resolution GFW maps of 21st-century forest cover change:
694 https://glad.earthengine.app/view/global-forest-change; MOD11C3 LST product:
695 https://lpdaac.usgs.gov/products/mod11c3v061/; MCD12C1 Land Cover dataset:
696 https://lpdaac.usgs.gov/products/mcd12c1v061/; MCD43C3 Albedo product:
697 https://lpdaac.usgs.gov/products/mcd43c3v061/; MOD16A2GF ET and LE product:
698 https://lpdaac.usgs.gov/products/mod16a2v061/; GLASS Shortwave Radiation product:
699 http://www.glass.umd.edu/Download.html; MOD13C2 NDVI product:
700 https://lpdaac.usgs.gov/products/mod13c2v061/; drivers of global forest loss:
701 https://www.science.org/doi/abs/10.1126/science.aau3445; GLASS ET product:
702 http://www.glass.umd.edu/Download.html; PML_V2 ET product:
703 https://data.tpdc.ac.cn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c/; the
704 latest digital Köppen-Geiger world map: http://koeppen-geiger.vu-
705 wien.ac.at/present.htm; global map of planting years:
706 https://figshare.com/articles/dataset/A_global_map_of_plantingYears_of_plantations
707 /19070084/1; forest cover change data of Canada:
708 https://opendata.nfis.org/mapserver/nfis-change_eng.html; forest cover change data of
709 northern Europe: https://land.copernicus.eu/pan-european/high-resolution-
710 layers/forests/tree-cover-density/change-maps; forest cover change data of eastern
711 Europe: https://glad.geog.umd.edu/dataset/eastern-europe-forset-cover-dynamics-
712 1985-2012/; forest cover change data of the USA: https://www.mrlc.gov/data; and
713 forest cover change data of the whole tropical region:
714 https://forobs.jrc.ec.europa.eu/TMF/.

715

716 Code Availability

717 No custom code was developed for this study. All equations were given in Methods.
718 The Origin (version 2022b) and ArcGIS (version 10.4) programs were used to generate
719 all results.

720

721 Conflict of Interests

The authors declare no competing interests.

723

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