Beyond Canopy Cover: How Tree Distribution Shapes Cloud Formation Across Africa

Beyond Canopy Cover: How Tree Distribution Shapes Cloud Formation Across Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Beyond Canopy Cover: How Tree Distribution Shapes Cloud Formation Across Africa Di Xie, Luca Caporaso, Markus Reichstein, Deyu Zhong, Gregory Duveiller This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5639740/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Vegetation plays a pivotal role in regulating climate and sustaining the hydrological cycle, with both the quantity and distribution of trees influencing surface and atmospheric processes. While the direct effects of vegetation on surface properties are well-documented, the indirect impacts of trees on clouds—especially those from trees outside the forest—are less explored, with spatial tree distribution often neglected. This study examines how tree cover, in terms of absolute coverage and spatial configuration, affects cloud formation over Africa. Our findings reveal distinct patterns of cloud sensitivity to tree cover changes across climatic zones and elevations, linked to energy partitioning during the day and land surface temperature disparities at night. Additionally, combining increases in tree cover and heterogeneity enhances cloud formation by 55.2% in tropical savannas compared to tree cover increase alone, underscoring the importance of strategic tree placement. This data-driven analysis enhances the understanding of vegetation-cloud interactions and provides valuable insights for tree restoration projects in Africa. Earth and environmental sciences/Ecology/Ecosystem ecology Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Biological sciences/Ecology/Forest ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Despite increasing efforts in environmental protection, land degradation continues to significantly impact human livelihoods worldwide (Prăvălie et al., 2024 ), particularly in developing countries across Asia and Africa (Barbier & Hochard, 2018 ). Africa, where approximately 75% of the land is classified as drylands, is particularly vulnerable to land degradation due to its limited water resources (Prăvălie, 2021 ). Rapid population growth in Sub-Saharan Africa has further intensified pressure on land resources (Maja & Ayano, 2021 ). Over recent decades, Africa has experienced substantial shifts in tree cover: deforestation is severe across tropical regions, primarily due to human-induced clearing (Aleman et al., 2017 ) while extensive woody plant encroachment happens in savannas, exacerbated by warming and wetting climates (Venter et al., 2018 ; Buitenwerf et al., 2012 ). Land restoration projects in Africa have also surged in recent years, promoting greening across the continent (Martin et al., 2021 ; Ruijsch et al., 2023 ). Another noteworthy aspect is that a significant proportion of dryland trees grow in farmlands, savannahs, and deserts outside traditional forest regions in Africa. These non-forest trees play a crucial role in providing ecosystem services and affect the climate by lowering albedo, altering aerodynamic roughness, and modulating transpiration (Brandt et al., 2020 ). However, the contributions of these trees to livelihoods and their impacts on climate have often been underestimated and overlooked. Recently, non-forest trees have gained increasing focus in environmental research and initiatives across Africa. Reiner et al. ( 2023 ) quantified the contribution of trees outside forests in Africa, revealing that at the continental scale, 29% of all tree cover is found outside areas classified as forests in current state-of-the-art maps. This high-resolution tree cover map provides a valuable opportunity to evaluate the biophysical impacts of total tree cover distribution rather than forest cover. Changes in tree cover can significantly impact hydrometeorological processes via biophysical mechanisms at various scales (Perugini et al., 2017 ; Foley et al., 2005 ). These mechanisms are closely associated with properties of the land surface such as albedo, roughness, and conductance, which influence how energy and water are exchanged between the earth and the atmosphere (Bonan, 2008 ). Additionally, tree cover changes can have indirect biophysical effects on the climate, which do not stem from changes in the properties of the surface directly, but rather from the atmospheric boundary layer (ABL) through land-atmosphere interactions (Duveiller et al., 2021 ). A typical example is the increased formation of low-level convective clouds above forests in some regions (Teuling et al., 2017 ; Dror et al., 2021 ). Increased cloud cover can further affect the water cycle by triggering an increase in precipitation (Pielke, 2001 ). While the direct effects of tree cover change on surface temperatures have been thoroughly examined, their indirect effects on cloud formation and precipitation have received much less attention. Process-oriented Earth system models are considered ideal for studying land-atmosphere interactions due to their comprehensive consideration of the Earth system. Model-based studies have reported both an increase and decrease in cloud cover as a result of afforestation (Portmann et al., 2022 ; Laguë et al., 2016; Shukla & Mintz, 1982 ) and a decrease in rainfall following deforestation (Spracklen & Garcia-Carreras, 2015 ; Lawrence & Vandecar et al., 2015). However, models still face challenges in accurately simulating certain cloud-related processes (Potter & Cess, 2004 ; Wang et al., 2022 ; Hannak et al., 2017 ) and display substantial variability due to differences in land surface schemes and the ways land cover changes are implemented (Pitman et al., 2009 ; Boysen et al., 2020 ). Advances in computational power are gradually improving the ability of models to capture complex biophysical effects at kilometric scales—a resolution critical for designing effective land restoration strategies. However, model outcomes can still blend localized biophysical mechanisms with broader non-local feedback from land cover changes, complicating direct comparisons with observations (Chen & Dirmeyer, 2020 ). Satellite remote sensing offers a promising alternative, providing consistent and concurrent measurements of cloud and land cover changes. Assessments from satellite observations and models have shown large agreement on the positive impacts of European forests on cloud cover (Caporaso et al., 2024 ). Furthermore, data-driven studies have offered even more complete and detailed observational evidence for the varied effects of forests on clouds. For instance, research indicates that afforestation typically increases low cloud cover in certain areas (Gambill & Mecikalski, 2011 ; Teuling et al., 2017 ; Duveiller et al., 2021 ). Conversely, deforestation in the Amazon appears to increase clouds and precipitation, likely due to the increase in the heterogeneity of the land surface that triggers mesoscale circulations thermally (Negri et al., 2004 ; Wang et al., 2009 ) and contributes to contrasting surface roughness (Khanna et al., 2017 ). However, these effects are highly scale-dependent, with large-scale deforestation typically producing negative impacts on cloud cover and precipitation (Lawrence & Vandecar, 2015 ; Pitman & Lorenz, 2016 ; Smith et al., 2023 ). Further, Xu et al. ( 2022 ) point out that the impact of forests on cloud cover varies by region and correlates with sensible heat emissions: more cloud formation is observed over forests emitting more heat, while less cloud activity is noted over cooler forests. These empirical findings, which show contrasting cloud effects over vegetation across the world, are not fully present in model-based analysis. Inconsistencies between model results and observations are related to parameterization issues (Li et al., 2018 ), scale differences (Pitman & Lorenz, 2016 ), differences in background climate conditions (Pitman et al., 2011 ), and the effects of indirect climate feedback that are not detectable using observation-based approaches (Chen & Dirmeyer, 2020 ). Such discrepancies between model predictions and observational data underscore the substantial uncertainties in how clouds and convection are represented in climate models (Bony et al., 2015 ; Schneider et al., 2017 ) and highlight the complex, scale-dependent nature of vegetation-cloud interactions. Several mechanisms have been proposed to explain the varying effects of trees on cloud cover. On the one hand, differences in surface properties induced by tree cover create spatial variations in energy availability and the partitioning between sensible and latent heat fluxes, influencing the evolution of the ABL and convective clouds development (Betts, 2000 ; Heiblum et al., 2014 ). On the other hand, roughness, largely related to tree cover heterogeneity, has also been identified as an important factor in controlling not only the surface energy balance and boundary-layer state (Harman, 2012 ) but also leading to enhanced turbulence and frictional convergence by slowing air masses (Rieck et al., 2014 ; Teuling et al., 2017 ). Furthermore, the differing heating rates between vegetated and non-vegetated areas can create sea-breeze-like secondary circulations, resulting in mesoscale wind convergence that supports cloud development (Garcia-Carreras et al., 2010 ; Lee et al., 2019 ; Tian et al., 2022 ). Also, soil moisture heterogeneity strongly influences convective cloud and rainfall patterns, particularly in the Sahel and other semi-arid tropical regions (Taylor et al., 2011 ). Thus, both tree cover and heterogeneity can be expected to significantly impact cloud formation. However, observational evidence confirming the influence of land surface heterogeneity on clouds remains limited, and the combined effects of these mechanisms are not fully understood. Additionally, much of the current research focuses on forested versus non-forested areas or the impacts of afforestation and deforestation, often overlooking the role of trees outside forests in cloud formation, which warrants further investigation. In this study, we aim to assess the impact of tree distribution on cloud formation across Africa, considering both the effects of average tree cover and spatial heterogeneity, as well as their confounding interactions. We use high-resolution tree cover (TC) maps from Reiner et al. ( 2023 ), which map both forest and non-forest tree cover for continental Africa, allowing us to account for the full impact of all trees rather than just forests. Furthermore, using tree cover directly avoids the issue of inconsistent forest definitions (Zalles et al., 2024 ), which can significantly affect the reliability of results. To capture the heterogeneity of tree cover distribution, we use Rao’s Q index (Rocchini et al., 2017 ), which indicates the potential for similar tree density around a given pixel. Based on the details of tree cover distribution, we apply a "space-for-time" substitution method over a moving window (Li et al., 2015 ; Duveiller et al., 2018 ; Li et al., 2023 ) to derive the sensitivity of cloud fraction cover (CFC) to both tree cover and spatial heterogeneity at different times of the day, relying on CFC data obtained from the geostationary satellites of the Meteosat Second Generation (MSG) program. Using this method, we minimize the effect of natural climate variability, focusing solely on the impacts of local vegetation contrasts. This enables us to provide an observational assessment of the impact of tree cover on cloud formation, eliminating the confounding effects of heterogeneity across Africa and offering an estimation of local cloud cover changes under different plausible future tree-planting scenarios that consider both tree quantity and spatial distribution. Additionally, we analyze the response of surface properties—including sensible heat flux (SHF), latent heat flux (LHF), and land surface temperature (LST)—to tree cover changes, comparing the mean state differences that drive cloud formation and exploring the potential mechanisms for vegetation-cloud interactions. Results Biophysical sensitivity of cloud cover to tree cover As detailed in the ‘Methods’ section, we conducted a multivariable regression to derive the regression coefficient for tree cover ( \(\:{\beta\:}_{TC}\) ), Rao’s Q ( \(\:{\beta\:}_{Q}\) ), and their interaction term ( \(\:{\beta\:}_{TC,\:\:Q}\) ). The coefficient \(\:{\beta\:}_{TC}\) indicates the biophysical sensitivity of cloud cover to tree cover under average Rao’s Q for each tree cover bin, with the effects of heterogeneity eliminated. Figure 1 illustrates the annual \(\:{\beta\:}_{TC}\) values for daytime (13:00–15:00, local time) and nighttime (4:00–6:00, local time) cloud cover, indicating the potential annual mean cloud response to one standard deviation (29.3%) change in tree cover. A comparison of the average regression coefficients is further provided in Fig. S1 . The use of geostationary satellite data enables calculations at an hourly resolution, and the selected hours here are expected to capture maximum daytime convection and maximum nighttime stability. We also calculated the sensitivity for full daytime (6:00–18:00, local time) and nighttime (18:00–6:00, local time) averages presented in Fig. S2. While similar spatial patterns emerge in the full-period averages, the analyses focused on specific hours reveal stronger signals. A positive \(\:{\beta\:}_{TC}\) indicates that cloud presence increases with more tree cover and a negative value indicates an opposite direction of change. As shown in Fig. 1 , approximately 61.1% of the vegetated area shows a positive sensitivity of daytime cloud cover to tree cover (Fig. 1 a). However, for nighttime cloud cover, the proportions of positive and negative effects are similar, with 53.5% of locations showing positive effects (Fig. 1 b). Spatially, strong daytime cloud enhancement occurs primarily near the equator in the central Congo basin and the marginal belts near the Sahara and Kalahari deserts. However, some areas at the boundary of the Congo Basin and the Chad Basin exhibit cloud reduction. At night, the impacts in the rainforest region are quite small, and the cloud cover is more responsive to increased tree cover in the arid regions, particularly near desert margins, with extensive negative effects observed in the plateau region in southern Africa around 10°S. Overall, the strength of cloud enhancement varies with latitude during the daytime, showing a latitude-averaged pattern of increased cloud cover with higher tree cover. In contrast, nighttime cloud effects exhibit more variable patterns of cloud enhancement and reduction across different latitudes (Fig. 1 c). This latitude-dependent variability underscores the complexity of cloud formation processes and their sensitivity to tree cover, influenced by both diurnal and regional climatic conditions. The coefficient \(\:{\beta\:}_{TC}\) also exhibits strong seasonality. In equatorial regions, daytime sensitivity is generally positive, peaking from September to December (Fig. 1 d). In contrast, 5°S to 15°S shows season-dependent variability, with positive daytime cloud sensitivity dominant from September to November and negative sensitivity from March to May. This variation is largely driven by contrasting patterns in the plateau region of southern Africa, including the Lunda Plateau, Katanga Plateau, and Bié Plateau, etc, a trend that may characterize higher-latitude regions. At night, strong negative sensitivity is observed between 10°S and 15°S from June to September (Fig. 1 e). Additionally, equatorial regions experience cloud reduction effects, particularly from December to February. The marginal belts near the desert, between 10°N and 15°N, display varying cloud effects across different months. These seasonal contrasts in cloud cover and the differing responses between day and night highlight the need for further research to understand the underlying mechanisms driving these variations, particularly in regions with diverse climatic and geographical features. Figure 2 illustrates the \(\:{\beta\:}_{TC}\) values across various climate conditions and topographical situations during the wet season (the wettest 3 months of the year) and dry season (the driest 3 months of the year), both calculated for each pixel individually. In tropical rainforest regions, primarily located in central Africa (Fig. 2 a), daytime and nighttime sensitivities exhibit opposite trends (Fig. 2 b). This contrast further suggests that vegetation differences drive cloud variability, as cloud effects diverge across day and night despite similar climatic conditions. For the magnitude, the daytime cloud enhancement effect is more than 3 times stronger than the nighttime reduction. Additionally, daytime sensitivity is more pronounced during the wet season than in the dry season, while at night, the pattern reverses. This seasonal difference is even marked in tropical monsoon regions. In tropical savannahs, the daytime vegetation-cloud effect is smaller than in tropical rainforest regions. Also, nighttime cloud effects in savannahs show seasonal contrasts, indicating more pronounced seasonal variability compared to rainforests. In summary, the analysis reveals distinct variations in cloud sensitivity across climatic zones and seasons, underscoring the complexity of climate interactions in different geographical contexts. It is important to note that the estimated cloud effects of forests could be modified by orographic conditions, given the dual influences of topography on both tree distribution and cloud formation. Figure 2 c shows the distribution of elevation in Africa and Fig. 2 d illustrates the average \(\:{\beta\:}_{TC}\) across seven elevation categories ranging from 0 to 3,000 m. Notably, higher elevations generally exhibit more pronounced positive sensitivity to tree cover during the wet season, with daytime values exceeding nighttime values. During the dry season, daytime cloud sensitivity remains positive but is relatively consistent across elevation categories, except for elevated areas between 500–750 m and above 1,500 m. For nighttime clouds, negative sensitivity dominates and decreases with elevation, particularly in regions above 2,000 m. These high-elevation variations help explain the seasonally diverse cloud patterns observed in the plateau region (Fig. 1 e), which indicate a strong suppressive effect of tree cover on nighttime cloud formation at high elevations under water-limited conditions. Potential mechanisms connecting cloud sensitivity and tree cover While various biophysical processes are involved in the interactions between trees and clouds, it remains unclear which factors determine the spatial patterns of cloud enhancement and reduction during day and night. In terms of biophysical differences, regions with dense tree coverage generally exhibit lower albedo, higher surface roughness, lower land surface temperature (LST), and increased evapotranspiration compared to short-vegetation systems (Li et al., 2015 ; Duveiller et al., 2018 ). However, these differences may impact the cloud in divergent ways under different conditions. Figure 3 illustrates the sensitivity of daytime sensible heat flux (SHF) and nighttime land surface temperature (LST) to changes in tree cover (i.e. \(\:\:{\beta\:}_{TC}\:\) for SHF and LST), with the sensitivities for other variables presented in Fig. S3 and Fig. S4. The LST data used in the study is the all-sky data derived from clear-sky LST with cloudy pixels filled by the skin temperature from the energy balance model, therefore the LST under the cloud can also be analyzed. Increased tree cover across Africa generally leads to higher daytime latent heat flux (LHF) during both wet and dry seasons, suggesting greater moisture availability for cloud formation (Fig. S3c and Fig. S3g), while daytime LST consistently decreases due to the cooling effects of trees (Fig. S3d and Fig. S3h). However, SHF exhibits distinct patterns: it increases with more tree cover in central forest regions but decreases in savannahs surrounding rainforests; in arid regions, SHF sensitivity shows positive values during the dry season but reverses in the wet season (Fig. 3 a and Fig. 3 b). This pattern arises because greening typically results in a greater increase in LHF in warm and dry regions due to increased evaporative surfaces, causing SHF to change in the opposite direction (Forzieri et al., 2020 ). In these regions, despite potential soil moisture limitations, increased tree cover enhances LHF through complex tree adaptations, such as root development and groundwater access (Fan & Miguez-Macho, 2013). Moreover, the opposite SHF trends in arid regions near deserts during the wet and dry seasons are primarily driven by moisture supply, which regulates transpiration. Existing studies confirmed that SHF variations largely influence cloud formation (Bosman et al., 2019 ; Xu et al., 2022 ). Figure 3 e and Fig. 3 f display the spatial distribution of the direction of daytime \(\:{\beta\:}_{TC}\) for CFC and SHF, where point colors denote the relationship between these two variables, and point size reflects the magnitude of cloud sensitivity. In the wet season, positive synchrony (green points) is prominent in rainforest regions, constituting 24.3% of pixels. Negative synchrony (blue points, 23.0%) appears mainly in tropical savannahs surrounding forests and parts of the temperate zone. Cloud formation requires both a lifting mechanism (from SHF) and sufficient moisture (from LHF). Therefore, in these relatively humid regions, greater SHF fosters turbulent mixing and boundary layer development (Fisch et al., 2004 ), promoting upward air motion and cloud formation (Gentine et al., 2013 ; Bosman et al., 2019 ). However, opposing CFC and SHF trends (red points, 36.2%) are prominent in arid regions and part of temperate zones, extending even to tropical savannahs in southern Africa. In these water-limited regions, SHF is already adequate to induce the lifting of local moisture, and tree cover primarily enhances LHF, providing extra moisture and supporting cloud formation even though SHF decreases. In dry seasons, the vegetation-cloud effect is strong in central Africa, with positive synchrony remaining prevalent in tropical rainforests. However, surrounding savannahs exhibit both yellow and blue dots, suggesting that while SHF sensitivity varies, cloud reduction persists, which implies the moisture limitation in savannas in the dry season. At nighttime, LHF still increases across Africa with more tree cover, albeit to a smaller extent (Fig. S4c and Fig. S4g). In contrast, nighttime SHF mainly decreases due to the lack of incoming shortwave radiation at night (Fig. S4b and Fig. S4f), making the impacts of surface energy fluxes generally negligible under these conditions. However, nighttime LST exhibits distinctive patterns. Nighttime LST increases with more tree cover across the tropical rainforest regions, especially during the dry season. Forests, with their high surface roughness from taller canopies, promote greater mixing and heat dissipation during the day but can act as a heat "trap" at night (Lee et al., 2011 ). As a result, central forests can better maintain warmer surface temperatures at night when tree cover increases, particularly when transpiration is restricted in the dry season. In less humid regions, including tropical savannahs, arid steppes, and temperate zones, nighttime LST shows a negative relationship with TC in the dry season, while a positive sensitivity is observed in the wet season (Fig. 3 c and Fig. 3 d). In the wet season, soil with higher moisture content has a greater heat capacity, allowing more daytime heat storage (Peng et al., 2014 ). More trees contribute to heat aggregation processes in the daytime, slowing the nighttime temperature decline. The spatial distribution of \(\:{\beta\:}_{TC}\) for nighttime CFC and LST shows related patterns, especially during the dry season. Figure 3 g and 3 h illustrate the direction of \(\:{\beta\:}_{TC}\:\) for nighttime CFC and LST. In the dry season, substantial cloud effects appear, with orange and red points—indicating an inverse relationship between nighttime LST and CFC sensitivity—dominating at 39.7% and 24.0%, respectively (Fig. 3 h). This relationship suggests that cloud formation is more likely when nighttime LST decreases with increased tree cover, whereas cloud reduction occurs when LST increases at night. Cooler nighttime temperatures lead to nocturnal temperature inversion and higher relative humidity, increasing the likelihood of water vapor condensing into droplets and forming clouds when sufficient moisture is present (Dommo et al., 2022 ; Babić et al., 2019 ). Conversely, nighttime clouds can warm the surface by enhancing downward longwave radiation (Dai et al., 1999 ). This warming effect may be the dominant process in regions where LST sensitivity aligns with CFC sensitivity during the dry season. In the wet season, the nighttime vegetation-cloud effect is significant only in arid regions. Increased tree cover drives cloud formation regardless of whether the land surface cools or warms (Fig. 3 g), suggesting that the impact of LST diminishes under sufficient water availability. Estimation of cloud change under different tree cover change conditions In the previous section, we analyzed the individual impacts of tree cover change on cloud formation. However, changes in tree cover are typically accompanied by alterations in tree cover heterogeneity, which also influence cloud formation. Therefore, we calculated changes in cloud fraction cover (CFC) under specific modifications in average tree cover and Rao’s Q to assess the combined roles of tree cover and heterogeneity in cloud formation. In this analysis, average tree cover was assumed to change by 20% of the maximum tree cover on the map, while Rao’s Q was adjusted by 20% of the maximum value within each 1% tree cover bin. Pixels exceeding the maximum or falling below the minimum limits were adjusted accordingly. Figure 4 displays daytime cloud cover changes across Africa under varying tree cover and Rao’s Q scenarios. For the original Rao’s Q, a 20% decrease in tree cover primarily reduces cloud cover, with localized increases in savannahs (Fig. 4 d). When heterogeneity is reduced simultaneously, the cloud reduction becomes more pronounced, especially in arid steppe regions (Fig. 4 a). Conversely, increasing heterogeneity strengthens cloud inhibition in tropical rainforests and enhances cloud cover in savannahs (Fig. 4 g). A 20% increase in tree cover significantly enhances cloud cover in arid and temperate zones but reduces it in some savannahs (Fig. 4 f). Decreased heterogeneity amplifies this reduction (Fig. 4 c), whereas increased heterogeneity heightens cloud sensitivity to tree cover changes (Fig. 4 i). The center column illustrates the scenario where heterogeneity changes while tree cover remains constant, representing a hypothetical rearrangement of trees into either more compact, structured configurations or more randomly distributed patterns. In tropical rainforests and surrounding densely vegetated areas, greater heterogeneity is associated with reduced cloud cover, whereas in savannahs and arid steppe regions, increased heterogeneity enhances cloud formation (Fig. 4 b and Fig. 4 h). These findings reveal distinct regional variations in how tree cover and heterogeneity influences cloud formation, which may be influenced by background climate, particularly the baseline percentage of tree cover within each region or pixel. In areas with already high forest density, even minor changes could significantly disrupt local microclimates and affect cloud formation. Although findings from Wang et al. ( 2009 ) indicate that increased heterogeneity in deforested regions contributes to shallow cloud formation in the Amazon, it remains uncertain whether this effect will persist under deforestation at larger scales. On average, we observe a 55.2% increase in the cloud enhancement effect when tree cover and heterogeneity are considered together, compared to the effect of tree cover increase alone in tropical savanna regions, and this value is 12.4% in arid steppe zones (Fig. 4 j). Increased heterogeneity enhances surface roughness, contributing to turbulence and frictional convergence. Additionally, the contrast between trees and non-trees influences the formation of mesoscale circulation, further enhancing cloud formation. Generally, heterogeneity’s influence on cloud formation shifts with average tree cover: it positively impacts clouds at low tree cover but turns negative as cover increases. These variations highlight the intricate relationship between tree cover, heterogeneity, and cloud formation, underscoring the importance of considering both tree numbers and spatial configuration in the context of planting trees. The calculation is also applied for nighttime cloud cover (Fig. S5). Under the original tree cover conditions, increased heterogeneity generally promotes cloud formation at night, except for high-elevation regions (Fig. S5h), while decreased heterogeneity typically reduces cloud formation (Fig. S5b). When tree cover decreases, positive cloud effects are observed in high-elevation regions of southern Africa, while other regions exhibit negative effects (Fig. S5d). Further reductions in heterogeneity will amplify this pattern (Fig. S5a). Conversely, an increase in Rao’s Q under these conditions significantly shifts cloud cover dynamics, resulting in positive changes across most regions (Fig. S5g). For tree cover increases, arid regions remain the most sensitive, experiencing substantial increases in cloud cover. However, high-elevation areas in southern Africa again exhibit decreases in cloud cover (Fig. S5f). Additional heterogeneity further enhances cloud increases in arid regions (Fig. S5i), while reduced heterogeneity expands areas with negative cloud cover effects, even with increased tree cover (Fig. S5c). Since nighttime cloud cover is closely linked to LST, the distinct temperature feedbacks in high-elevation regions may explain their differing responses to changes in heterogeneity. Discussion and perspectives In this study, we used a space-for-time approach to investigate local cloud effects of tree cover distribution change in Africa, incorporating both absolute coverage and spatial configuration, which advances our understanding of vegetation–cloud interactions by revealing key patterns across various regions. As for the individual impacts of increased tree cover, our findings show that increased daytime cloud cover occurs over the tropical rainforest and the arid steppe regions, whereas a reduction in cloud formation is observed over tropical savannahs. At night, a stronger negative relationship between tree cover and cloud formation is observed during the dry season, particularly in the high-elevation regions of southern Africa. Mechanistically, the spatial variation in cloud formation is linked to sensible heating during the day in regions where water is sufficient. In water-limited regions, however, cloud formation responds differently as moisture availability becomes the controlling factor. At night, cloud effects are more related to land surface temperature differences induced by tree cover, likely due to water condensation on cooler surfaces. When considering spatial configuration, cloud predictions under different scenarios indicate that greater heterogeneity enhances cloud formation in savannah and arid steppe regions as tree cover increases. Conversely, in tropical rainforest regions, increased heterogeneity amplifies the reduction in cloud cover caused by declining tree cover. This finding implies that when planning tree restoration in savannah regions of Africa, trees should be distributed in more random, heterogeneous arrangements to maximize cloud cover. The corollary to this suggested by our results is that deforestation in tropical forest regions would lead to a greater reduction in cloud cover if it occurs in a sparse manner, akin to what would happen following selective logging, rather than equivalent reductions in forest cover concentrated in clear cuts. Future monitoring should consider varying conditions across the degradation-deforestation continuum (Lapola et al., 2023 ), as these differences can also significantly impact atmospheric processes. These insights highlight that the method of afforestation or deforestation is as important as the location of tree planting, providing valuable guidance for the planning and implementation of future tree restoration projects in Africa. This study provides continent-scale observational evidence of cloud sensitivity to tree cover and enhances our mechanistic understanding of vegetation-cloud interactions. The cloud effects estimated in our study reflect the local impact of tree cover on cloud formation, offering a more realistic representation of fine-scale changes in tree cover. By incorporating surface heterogeneity, we also highlight the role of small-scale turbulence, frictional convergence, and mesoscale circulation—factors that are typically unresolved in global climate models. While cloud processes are inherently complex, and the cloud cover observations used in this study offer only a simplified view, our analysis serves as an approximation of vegetation-cloud interactions and can act as a reference for model simulations. This can help constrain and calibrate models, thereby improving their ability to derive a more nuanced understanding of the mechanisms behind the consequences of changes in the biophysical properties of land. Several uncertainties must be considered. A key source of uncertainty is the cloud data. We utilized full cloud fraction cover data, which includes low, medium, and high-level clouds. Low-level clouds, which form within the boundary layer, are considered to be more likely to exhibit a strong spatial correlation with the underlying surface (Duveiller et al., 2021 ). Considering all cloud types together may introduce random noise from medium- and high-level clouds, which are not directly related to vegetation patterns. However, high-level deep convection clouds are also expected to be influenced by the surface (Xu et al., 2022 ), which should not be neglected. Additionally, labeling cloud types in total cloud cover maps based on cloud classification schemes adds further uncertainty, as most cloud types identified by ISCCP (International Satellite Cloud Climatology Project) joint histograms (Rossow & Schiffer, 1991 ) do not correspond to single-layered or uniquely defined geometric cloud types (Mace & Wrenn, 2013 ). As a result, cloud classification complicates the analysis, making outcomes highly sensitive to the accuracy of cloud separation. Since our study focuses on periods when cumulus and stratocumulus clouds are at their peak (Eastman & Warren, 2014 ), the results likely reflect surface influences. To further address this, the cloud profile retrievals from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat could be applied to better differentiate between various cloud phases and types. Additionally, our method captures only a fraction of the local effects of tree cover distribution on cloud formation. Processes such as advection may further obscure the signal (Chen et al., 2020 ), complicating efforts to isolate the contribution of tree cover changes to cloud dynamics. Therefore, case studies with field measurements could serve as useful validation and complement existing results. Moreover, the observational nature of this study limits our ability to fully disentangle the various confounding factors that drive cloud formation. The complexity of cloud formation processes—such as boundary layer turbulence, mesoscale circulation, and frictional convergence—makes it challenging to identify the precise mechanisms involved (Teuling et al., 2017 ). Lastly, the study area for this paper is limited to Africa, with data from the year 2019, constrained by the availability of high-resolution tree maps and geostationary satellite cloud data. In the future, multi-year studies could be conducted across multiple regions globally, such as the United States, China, and Europe, where geostationary satellite data is available. Such explorations will provide a more comprehensive assessment of how tree cover patterns influence cloud formation. As an extension, the tight coupling between cloud and precipitation processes suggests that changes in tree cover could significantly impact precipitation patterns (Roy, 2009 ; Garcia-Carreras & Parker, 2011 ; Hartley et al., 2016 ). Directly observing the effects of deforestation on precipitation is challenging. However, high-resolution satellite data on cloud impacts can provide valuable insights into potential precipitation changes, particularly in tropical regions dominated by convective rainfall. Although our analysis does not directly relate cloud formation to rainfall, we can anticipate that the cloud formation we see could strengthen the hydrological value of vegetation. Additionally, changes in tree cover due to deforestation or afforestation affect not only local climate and hydrology but also have remote impacts on precipitation, runoff, and water availability in distant regions through mechanisms like moisture recycling and advection (Van Der Ent et al., 2010 ; Wang-Erlandsson et al., 2018 ; Hoek Van Dijke et al., 2022). Accurate predictions of these impacts require a better understanding of vegetation–cloud interactions. To explore the full picture, including both local and nonlocal effects, the only option is to integrate experimental evidence with the formal representation of processes in Earth system model experiments. Methods Input data and pre-processing The primary data for this study originate from distinct satellite remote sensing products. For Cloud Fraction Cover (CFC), we use the CLAAS-3 dataset, derived from Spinning Enhanced Visible and InfraRed Imager (SEVIRI) observations onboard the Meteosat Second Generation (MSG) geostationary satellites, operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) (Meirink et al., 2022 ). This dataset includes 15-minute cloud detection data over a fixed disk covering Europe, Africa, and Eastern South America, with a spatial resolution that ranges from 3 km at nadir up to approximately 5 km at the disk edge (Benas et al., 2023 ). The probabilistic cloud mask retrieved using a naive Bayesian approximation from level 2 data is used here. Additionally, half-hourly datasets for Latent Heat Flux (LHF), Sensible Heat Flux (SHF), and Land Surface Temperature (LST) from MSG geostationary satellites (Trigo et al., 2011 ), with a resolution of 0.05°, are used to assess surface processes related to cloud formation. The LHF and SHF data are derived by solving the surface energy balance at the tile level, following the TESSEL (Tiled ECMWF Scheme of Surface Exchange over Land) model (Van den Hurk et al., 2000 ). These datasets of surface energy fluxes have demonstrated good coherence with eddy covariance data and other global/continental-scale products (Barrios et al., 2024 ). For LST, we use the all-sky LST, which combines clear-sky LST retrieved from MSG infrared measurements with LST estimated via the same land surface energy balance model used to derive surface energy fluxes. This method fills gaps caused by cloud cover and provides accurate LST estimates compared to in-situ LST obtained under all-sky conditions (Martins et al., 2019 ). The original datasets were processed into monthly averages of hourly values at a 0.05° resolution for the year 2019. The original MSG cloud data in Coordinated Universal Time (UTC) were adjusted to local time before analysis. For Tree Cover (TC), the high-resolution tree cover dataset for Africa in 2019, provided by Reiner et al. ( 2023 ), is used to calculate the average TC condition and to assess spatial heterogeneity with a 0.05° spatial resolution. This dataset is based on 3 m spatial resolution satellite imagery from Planet Labs and uses deep learning techniques to accurately map tree cover down to individual trees. In this study, we used the map aggregated to a spatial resolution of 100 m and considered areas on the African continent where tree coverage exists. As a result, the desert regions and open water areas were excluded. To analyze the impacts of climate conditions on cloud-vegetation sensitivity, we adopted the Köppen-Geiger climate classification (Peel et al., 2007 ) and classified the African continent into eight climatic zones (i.e., tropical rainforest, tropical monsoon, tropical savannah, arid desert, arid steppe, temperate zone, cold zone, and polar zone) according to the climate classification map from Beck et al. ( 2018 ). To filter out the impact of altitude on the local cloud and discuss the topological impacts on cloud-vegetation sensitivity, we used a digital elevation model (DEM) with a 0.05° spatial resolution, aggregated from the version 4 Shuttle Radar Topography Mission (SRTM) with a 90 m resolution. Calculation of tree cover heterogeneity Using the 100-meter tree cover map, we apply the entropy-based Rao’s Q index (Q) to quantify the tree cover heterogeneity. Entropy-based indices are commonly employed to evaluate the heterogeneity and complexity of spatially arranged data in ecological studies (Parrott, 2010 ; Altieri et al., 2018 ; Cushman, 2015 ). When translating such methods from species counts to pixel values, some changes are warranted. The widely-used classical Shannon entropy index (Shannon, 1948 ), accounts for richness and relative abundance of classes of spectral values but does not explicitly consider the numerical magnitude (values) of pixels. On the contrary, Rao’s Q index takes into account the value of pixels by considering their pairwise differences, here considered as a distance (Rocchini et al., 2017 ). For each pixel, a surrounding window is selected to calculate Rao’s Q as follows: \(\:Q=\:\sum\:\sum\:{d}_{ij}\times\:{p}_{i}\times\:{p}_{j}\) (1) This index is based on classes of TC at the scale of 100 m pixels. TC values (in percentage) are rounded to the nearest integer to assign their class, thus resulting in classes with a range of 1%. The subscripts i and j represent different classes of TC within the window. \(\:{p}_{i}\) is defined as the ratio of pixels falling in the class representing value i to the total number of pixels in the window; \(\:{d}_{ij}\) represents the pairwise distance between the classes of value i and j , calculated as the absolute value of the difference between i and j . Based on its definition, Rao’s Q represents the expected difference in tree cover values between two pixels drawn randomly with replacement from the considered window, indicating the potential for similar tree density surrounding the target pixel. Therefore, a small value of Rao’s Q indicates homogeneous tree cover, while a high value indicates heterogeneity. In this study, we used a moving window of 5 × 5 pixels (representing a region of 500 × 500 m) for each pixel and generated the distribution map of Rao’s Q at a spatial resolution of 100 m. The spatial distribution of trees in Africa, along with examples of tree cover and Rao’s Q within a 0.05° × 0.05° window, is shown in Fig. 5 . In terms of tree cover, regions with high tree cover are primarily located in the forested areas of central Africa, while the surrounding savannah regions exhibit lower tree cover (Fig. 5 a). In contrast, Rao’s Q values are higher in the savanna regions and some temperate zones, where tree cover is lower compared to the Congo Basin, which displays a more uniform tree cover distribution (Fig. 5 b). The sample regions A, B, C, and D, located in different climate zones, have average tree cover values of 82.89%, 46.13%, 32.56%, and 3.42%, respectively. Their corresponding average Rao’s Q values are 8.12%, 11.16%, 7.00%, and 1.40%, respectively (Fig. 5 c). This figure suggests that Rao’s Q varies with both average tree cover and distribution patterns, which together impact the cloud formation. Removal of tree cover-heterogeneity relationship To match the spatial resolution of the cloud cover data, the original tree cover and the derived Rao’s Q map, both in 100 m resolution, are further aggregated to 0.05°. Since the average tree cover and Rao’s Q both originate from the 100 m tree cover map, there is an inherent quadratic relationship between the two variables, as illustrated in Fig. 6 a. When the average tree cover approaches 0% or 100% coverage, Rao's Q approaches 0%, indicating homogeneity with either no trees or complete tree coverage. Conversely, when the average tree cover is between these extremes, particularly at 50%, Rao’s Q values are high and exhibit considerable variability. To isolate the individual impacts of tree cover and tree heterogeneity, we scale Rao’s Q using Z-score normalization within each 1% tree cover bin (Fig. 6 b). This ensures that pixels share relatively comparable Rao’s Q values across different levels of tree cover. We use this normalized Rao’s Q as the input for further analysis, and the normalized distribution, along with the corresponding mean and standard deviation for each pixel, is shown in Fig. S6. To make sure that tree cover and Rao’s Q are comparable in magnitude, the tree cover map is also scaled using Z-score normalization with a mean of 18.4% and a standard deviation of 29.3%. Estimation of cloud sensitivity to tree distribution In this study, we use a "space-for-time" method to calculate the biophysical sensitivity of clouds to average tree cover and spatial heterogeneity locally. This method is commonly applied to explore the local impacts of land use and land cover change on variables such as temperature and energy fluxes (Li et al., 2015 ; Duveiller et al., 2018 ; Li et al., 2023 ). The core assumption is that the target pixel shares the same background climate as adjacent pixels within a moving window. Therefore, any differences observed between the target and contrasting pixels are attributed to the biophysical feedback of local land cover change (Lee et al., 2011 ; Peng et al., 2014 ). Similarly, we assume that TC, Rao’s Q, and their interaction are the drivers of spatial variation in CFC under certain hydroclimate conditions when the elevation difference is controlled between pixels. This allows us to regress the biophysical sensitivity of clouds to tree distribution from spatially proximate observations. A key advantage of this method over temporal regression strategies is that it eliminates the influence of natural climate variability and long-term warming trends on vegetation growth, as pixels with varying TC and Rao’s Q values within the moving window are subject to the same background climate. The specific approach of this strategy works as follows: for each pixel from the TC, Rao’s Q and CFC map at a resolution of 0.05°, potential comparison samples are selected from spatially nearby pixels within a moving window, which is set to 9 × 9 pixels (representing approximately a region of 50 ×50 km), based on previous studies (Xu et al., 2022 ; Li et al., 2023 ). To minimize the influence of topography, we only consider pixels where the elevation difference between the selected pixels and the target pixel is less than 100 meters. Using this method, we can determine the biophysical sensitivity for the target pixel by regressing the differences in TC, Rao’s Q, and CFC between all selected comparison pixels and the target pixel. The scaled tree cover and the scaled Rao’s Q map are used as the input. To account for the individual impacts of TC and Rao’s Q, as well as their confounding effects, the regression is conducted using multivariable linear regression with interaction terms: \(\:CFC={\beta\:}_{0}+{\beta\:}_{TC}\times\:TC+{\beta\:}_{Q}\times\:Q+{\beta\:}_{TC,\:Q}\times\:TC\:\times\:Q+ϵ\) (2) Here, \(\:{\beta\:}_{0}\) is the intercept, and \(\:{\beta\:}_{TC}\:,\:{\beta\:}_{Q}\) and \(\:{\beta\:}_{TC,Q}\:\) are the coefficients for TC, Rao’s Q, and their interaction term; \(\:ϵ\) represents the random error. Additionally, we only calculate the sensitivity if there are at least 25% valid samples in each window and if the minimum TC difference is greater than 10%, ensuring the robustness of our results. Also, this “space-for-time” procedure is applied to sensible heat flux (SHF), latent heat flux (LHF), and land surface temperature (LST), together with tree cover to calculate the impacts of tree cover distribution on these variables. Using the spatial regression method described, we obtain the monthly biophysical sensitivity of CFC, LHF, SHF, and LST to TC at a 0.05° spatial resolution. To further exclude the impact from outliers, we remove the value within the maximum and the minimum 1% value based on the cumulative distribution frequency (CDF) for all the monthly results. Regressions are performed hourly for each month. Since cumulus clouds typically peak in the early afternoon and stratocumulus clouds form predominantly at the end of the night (Eastman & Warren, 2014 ), we focus on the hours from 13:00 to 15:00 to represent daytime sensitivity, as these hours are primarily influenced by convective clouds. Similarly, the hours from 04:00 to 06:00 are analyzed to capture nighttime sensitivity, when atmospheric conditions are most stable. This ensures that the detected signal can largely be attributed to vegetation properties on land. Seasonal analysis In addition to calculating the annual and monthly sensitivities, we also calculated changes for the dry season (the driest 3 months of the year), and the wet season (the wettest 3 months of the year). The driest and wettest months were identified for each pixel using the individual precipitation data from the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) Version 2 dataset (Funk et al., 2015 ) with a spatial resolution of 0.05°. Declarations Data availability The CLAAS-3 cloud dataset used in the study are available on EUMETSAT Climate Monitoring Satellite Application Facility program at https://wui.cmsaf.eu/safira/action/viewHome . The sensible heat flux, latent heat flux and land surface temperature are obtained on EUMETSAT Land Surface Analysis Satellite Application Facility program at https://lsa-saf.eumetsat.int/en/ . The Africa tree cover data from Reiner et al. ( 2023 ) are available for download at https://doi.org/10.5281/zenodo.7764460 . The SRTM DEM is available on the Google Earth Engine at https://developers.google.com/earth-engine/datasets/catalog/CGIAR_SRTM90_V4 . The Köppen-Geiger climate classification map is accessible via www.gloh2o.org/koppen . The CHIRPS Version 2 dataset are freely available to download from https://data.chc.ucsb.edu/products/?C=M;O=D . Competing interests The authors declare no competing interests. Author contributions D.X. led the conceptualization, methodology development, investigation, visualization, and writing of the original draft, as well as contributed to the review and editing. L.C. assisted with methodology development and contributed to the review and editing. M.R. played a role in reviewing and editing the manuscript and contributed to funding acquisition. D.Z. provided supervision and contributed to funding acquisition. G.D. played a central role in the conceptualization, methodology development, supervision, and editing. Acknowledgments This research was supported by the Topology of Hydrosphere Project by Key Laboratory of Hydrosphere Sciences of the Chinese Ministry of Water Resources (grant no. sklhse-TD-2024-F01, DZ). This research resulted from a research stay of D.X. in G.D.’s research group. This stay was supported by China Scholarship Council as No. 202306210299. G.D. and M.R acknowledge funding from the ERC Synergy Grant “Understanding and modeling the Earth System with Machine Learning (USMILE)” under the European Union's Horizon 2020 research and innovation program (grant agreement no. 855187) References Aleman, J. C., Jarzyna, M. A., & Staver, A. C. (2017). Forest extent and deforestation in tropical Africa since 1900. 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Water Resources Research, 46 (9), 2010WR009127. https://doi.org/10.1029/2010WR009127 Venter, Z. S., Cramer, M. D., & Hawkins, H.-J. (2018). Drivers of woody plant encroachment over Africa. Nature Communications, 9 (1), 2272. https://doi.org/10.1038/s41467-018-04616-8 Wang, H., Zhang, H., Xie, B., Jing, X., He, J., & Liu, Y. (2022). Evaluating the Impacts of Cloud Microphysical and Overlap Parameters on Simulated Clouds in Global Climate Models. Advances in Atmospheric Sciences, 39 (12), 2172–2187. https://doi.org/10.1007/s00376-021-0369-7 Wang, J., Chagnon, F. J. F., Williams, E. R., Betts, A. K., Renno, N. O., Machado, L. A. T., et al. (2009). Impact of deforestation in the Amazon basin on cloud climatology. Proceedings of the National Academy of Sciences , 106 (10), 3670–3674. https://doi.org/10.1073/pnas.0810156106 Wang-Erlandsson, L., Fetzer, I., Keys, P. W., Van Der Ent, R. J., Savenije, H. H. G., & Gordon, L. J. (2018). 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Supplementary Files CloudSupplementaryMaterials.docx Supplementary Materials for Beyond Canopy Cover: How Tree Distribution Shapes Cloud Formation Across Africa Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5639740","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392910581,"identity":"c3922dfe-b904-4986-a610-156c4b1fcfea","order_by":0,"name":"Di Xie","email":"","orcid":"","institution":"State Key Laboratory of Hydroscience and Engineering, Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Department of Hydraulic Engineering, Tsinghua University, Beijing, China; Max Planck Institute for Biogeochemistry, Jena, Germany","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Xie","suffix":""},{"id":392910582,"identity":"4fb25bcc-4466-44f5-8808-b2c2ec677a82","order_by":1,"name":"Luca Caporaso","email":"","orcid":"https://orcid.org/0000-0002-1370-693X","institution":"European Commission, Joint Research Centre, Ispra, Italy; National Research Council of Italy, Institute of BioEconomy, Rome, Italy","correspondingAuthor":false,"prefix":"","firstName":"Luca","middleName":"","lastName":"Caporaso","suffix":""},{"id":392910583,"identity":"bf0e70c3-3b08-42d9-a09e-a484368935ec","order_by":2,"name":"Markus Reichstein","email":"","orcid":"https://orcid.org/0000-0001-5736-1112","institution":"Max Planck Institute for Biogeochemistry, Jena, Germany","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Reichstein","suffix":""},{"id":392910584,"identity":"724e3b5b-d8f5-42c6-bc83-41f9312a2d5b","order_by":3,"name":"Deyu Zhong","email":"","orcid":"","institution":"State Key Laboratory of Hydroscience and Engineering, Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Department of Hydraulic Engineering, Tsinghua University, Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"Deyu","middleName":"","lastName":"Zhong","suffix":""},{"id":392910580,"identity":"d08c85d6-3ecc-4a4a-84a1-f4cb89abb7ee","order_by":4,"name":"Gregory Duveiller","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6471-8404","institution":"Max Planck Institute for Biogeochemistry, Jena, Germany","correspondingAuthor":true,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Duveiller","suffix":""}],"badges":[],"createdAt":"2024-12-13 17:07:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5639740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5639740/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74438634,"identity":"f5dc3450-b72e-4a53-9cd8-992580d9960d","added_by":"auto","created_at":"2025-01-22 09:45:47","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":443866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiophysical sensitivity of cloud cover to tree cover across Africa in 2019.\u003c/strong\u003e (a) Spatial map of annual daytime (averaged over 13:00-15:00, local time) cloud sensitivity to tree cover. (b) Spatial map of annual nighttime (averaged over 4:00-6:00, local time) cloud sensitivity to tree cover. The original result at a resolution of 0.05° has been aggregated to 1° for display. The pie chart in panels (a) and (b) shows the proportion of positive and negative values in the original 0.05° resolution. (c) Latitudinally averaged changes in both daytime and nighttime sensitivities. (d) Heatmap of monthly daytime cloud sensitivity by latitude. (e) is the same as (d), but for nighttime cloud sensitivity. Only latitude bands from 15°N to 25°S are shown, as regions outside this range were excluded due to insufficient data coverage. These figures are based on the coefficient \u003cem\u003eβ\u003c/em\u003e\u003csub\u003eTC\u003c/sub\u003e from the multiple regression results. The regression was conducted using the Ordinary Least Squares (OLS) function from the statsmodels library in Python, and only values with a p-value less than 0.05 were included.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/f9d766b2d679e8ac668fafd8.jpeg"},{"id":74438633,"identity":"303933f0-d87e-447e-ae9b-6033179ccbba","added_by":"auto","created_at":"2025-01-22 09:45:47","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":383211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpacts of climate zone and elevation on the sensitivity of cloud cover to tree cover.\u003c/strong\u003e (a) Spatial map for major Köppen−Geiger climate zones in Africa. (b) Averaged sensitivities for daytime and nighttime cloud fraction in wet season (the wettest three months of the year) and dry season (the driest three months of the year) across major Köppen−Geiger climate zones. Arid desert, temperate zone, cold zone, and polar zone are not considered here due to lack of vegetation or limited area. (c) Elevation distribution in Africa, with the inset showing the percentage of points within each elevation bin. (d) Average sensitivities of daytime and nighttime cloud fractions during wet and dry seasons across different elevation bins. The number of pixels in each elevation bin is indicated beside the heatmap. Regions with negative elevation comprise only 0.2% (2003 pixels) of Africa’s total area, and areas above 3,000 m make up just 0.1% (656 pixels) as shown in (c). Due to the lack of sufficient samples, these two categories are excluded here.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/3aff7a095fe8e43f88d81404.jpeg"},{"id":74438645,"identity":"860505df-e88a-49e6-821f-55296c6d4237","added_by":"auto","created_at":"2025-01-22 09:45:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":438061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between the sensitivities of surface properties and cloud cover to tree cover in 2019.\u003c/strong\u003e Sensitivity of daytime SHF to TC in the (a) wet season and (b) dry season. (c) and (d) are the same as (a) and (b) but for nighttime land surface temperature. The original result at a resolution of 0.05° has been aggregated to 1° for display in (a) - (d). The pie charts in (a) - (d) show the proportion of positive and negative values at the original 0.05° resolution. The distribution of directions for daytime SHF and CFC in (e) wet season and (f) dry season. (g) and (h) are the same as (e) and (f) but for nighttime LST. In panels (e)–(h), colors represent different combinations of sensitivity signs for Var1 (CFC) and Var2 (SHF or LST), while point size indicates the magnitude of the TC sensitivity of Var1 (CFC). The spatial map in (e) - (h) shows the distribution of these signs for data aggregated to 1°. The pie charts in (a) - (d) show the proportions of four combinations of signs at the original 0.05° resolution. These figures are based on the coefficient β\u003csub\u003eTC\u003c/sub\u003e from the multiple regression results. The regression was conducted using the Ordinary Least Squares (OLS) function from the statsmodels library in Python, and only values with a p-value less than 0.05 were included.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/53c757e43488d517b9ab44d6.jpeg"},{"id":74438641,"identity":"527ea8c0-5586-4b85-ac86-565183f05af1","added_by":"auto","created_at":"2025-01-22 09:45:47","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":496642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimation of daytime cloud cover changes due to alterations in tree cover and heterogeneity by 20%. \u003c/strong\u003e(a) Distribution of cloud cover change with decreased Rao’s Q and reduced tree cover. (b)–(d) and (f)–(i) represent similar distributions as (a) but under different combinations of change conditions, indicated by the text preceding each column and row. (e) The average daytime cloud cover condition in Africa. (i) Averaged values for daytime cloud cover change predictions across major Köppen−Geiger climate zones. The color bar on the left illustrates the cloud changes induced by alterations in tree cover and heterogeneity, applicable to panels (a)–(d) and (f)–(i), while the right color bar corresponds to the cloud cover shown in (e).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/3d4a4261cd7211f3263649c2.jpeg"},{"id":74439339,"identity":"f073cdfc-ce13-40f2-9baf-c9262d6214b0","added_by":"auto","created_at":"2025-01-22 09:53:47","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":435839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTree cover and Rao’s Q map across Africa in 2019\u003c/strong\u003e. (a) Spatial distribution of tree cover across Africa in 2019. (b) Spatial distribution of Rao’s Q. Points A, B, C, and D indicate example window locations. (c) Detailed 100 m tree cover maps and corresponding Rao’s Q distributions for windows A, B, C, and D, each covering 0.05° (approximately 55 × 55 pixels). The average tree cover and Rao’s Q values for each window are labeled on the respective maps.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/a78895def1c1f4f83307fc7d.jpeg"},{"id":74439343,"identity":"3645649f-5132-4c00-936d-b633847fc9b7","added_by":"auto","created_at":"2025-01-22 09:53:47","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":186465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship of tree cover and Rao’s Q.\u003c/strong\u003e (a) Heatmap showing the relationship between tree cover and corresponding Rao’s Q, with tree cover divided into 1% bins. The color intensity indicates the number of 0.05° pixels within each grid. (b) Heatmap of tree cover and Rao’s Q, scaled using Z-score normalization for each 1% tree cover bin.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/1be279ff70f544633d20005a.jpeg"},{"id":74592720,"identity":"5da60f7a-66b7-462c-9f4e-dd97b1576d2d","added_by":"auto","created_at":"2025-01-23 18:22:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3340782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/128baedc-9a23-44a4-98b9-038acdd4053a.pdf"},{"id":74438643,"identity":"01c8edc3-a115-4674-b292-b2ca5b5029b6","added_by":"auto","created_at":"2025-01-22 09:45:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4922852,"visible":true,"origin":"","legend":"Supplementary Materials for Beyond Canopy Cover: How Tree Distribution Shapes Cloud Formation Across Africa","description":"","filename":"CloudSupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5639740/v1/de7990485ed950977d23c2f0.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Beyond Canopy Cover: How Tree Distribution Shapes Cloud Formation Across Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite increasing efforts in environmental protection, land degradation continues to significantly impact human livelihoods worldwide (Prăvălie et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), particularly in developing countries across Asia and Africa (Barbier \u0026amp; Hochard, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Africa, where approximately 75% of the land is classified as drylands, is particularly vulnerable to land degradation due to its limited water resources (Prăvălie, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rapid population growth in Sub-Saharan Africa has further intensified pressure on land resources (Maja \u0026amp; Ayano, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Over recent decades, Africa has experienced substantial shifts in tree cover: deforestation is severe across tropical regions, primarily due to human-induced clearing (Aleman et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) while extensive woody plant encroachment happens in savannas, exacerbated by warming and wetting climates (Venter et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Buitenwerf et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Land restoration projects in Africa have also surged in recent years, promoting greening across the continent (Martin et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ruijsch et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another noteworthy aspect is that a significant proportion of dryland trees grow in farmlands, savannahs, and deserts outside traditional forest regions in Africa. These non-forest trees play a crucial role in providing ecosystem services and affect the climate by lowering albedo, altering aerodynamic roughness, and modulating transpiration (Brandt et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the contributions of these trees to livelihoods and their impacts on climate have often been underestimated and overlooked. Recently, non-forest trees have gained increasing focus in environmental research and initiatives across Africa. Reiner et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) quantified the contribution of trees outside forests in Africa, revealing that at the continental scale, 29% of all tree cover is found outside areas classified as forests in current state-of-the-art maps. This high-resolution tree cover map provides a valuable opportunity to evaluate the biophysical impacts of total tree cover distribution rather than forest cover.\u003c/p\u003e \u003cp\u003eChanges in tree cover can significantly impact hydrometeorological processes via biophysical mechanisms at various scales (Perugini et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Foley et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These mechanisms are closely associated with properties of the land surface such as albedo, roughness, and conductance, which influence how energy and water are exchanged between the earth and the atmosphere (Bonan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Additionally, tree cover changes can have indirect biophysical effects on the climate, which do not stem from changes in the properties of the surface directly, but rather from the atmospheric boundary layer (ABL) through land-atmosphere interactions (Duveiller et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A typical example is the increased formation of low-level convective clouds above forests in some regions (Teuling et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dror et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Increased cloud cover can further affect the water cycle by triggering an increase in precipitation (Pielke, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). While the direct effects of tree cover change on surface temperatures have been thoroughly examined, their indirect effects on cloud formation and precipitation have received much less attention.\u003c/p\u003e \u003cp\u003eProcess-oriented Earth system models are considered ideal for studying land-atmosphere interactions due to their comprehensive consideration of the Earth system. Model-based studies have reported both an increase and decrease in cloud cover as a result of afforestation (Portmann et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lagu\u0026euml; et al., 2016; Shukla \u0026amp; Mintz, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) and a decrease in rainfall following deforestation (Spracklen \u0026amp; Garcia-Carreras, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lawrence \u0026amp; Vandecar et al., 2015). However, models still face challenges in accurately simulating certain cloud-related processes (Potter \u0026amp; Cess, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hannak et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and display substantial variability due to differences in land surface schemes and the ways land cover changes are implemented (Pitman et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Boysen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Advances in computational power are gradually improving the ability of models to capture complex biophysical effects at kilometric scales\u0026mdash;a resolution critical for designing effective land restoration strategies. However, model outcomes can still blend localized biophysical mechanisms with broader non-local feedback from land cover changes, complicating direct comparisons with observations (Chen \u0026amp; Dirmeyer, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSatellite remote sensing offers a promising alternative, providing consistent and concurrent measurements of cloud and land cover changes. Assessments from satellite observations and models have shown large agreement on the positive impacts of European forests on cloud cover (Caporaso et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, data-driven studies have offered even more complete and detailed observational evidence for the varied effects of forests on clouds. For instance, research indicates that afforestation typically increases low cloud cover in certain areas (Gambill \u0026amp; Mecikalski, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Teuling et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Duveiller et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, deforestation in the Amazon appears to increase clouds and precipitation, likely due to the increase in the heterogeneity of the land surface that triggers mesoscale circulations thermally (Negri et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and contributes to contrasting surface roughness (Khanna et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, these effects are highly scale-dependent, with large-scale deforestation typically producing negative impacts on cloud cover and precipitation (Lawrence \u0026amp; Vandecar, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pitman \u0026amp; Lorenz, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Further, Xu et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) point out that the impact of forests on cloud cover varies by region and correlates with sensible heat emissions: more cloud formation is observed over forests emitting more heat, while less cloud activity is noted over cooler forests. These empirical findings, which show contrasting cloud effects over vegetation across the world, are not fully present in model-based analysis. Inconsistencies between model results and observations are related to parameterization issues (Li et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), scale differences (Pitman \u0026amp; Lorenz, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), differences in background climate conditions (Pitman et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the effects of indirect climate feedback that are not detectable using observation-based approaches (Chen \u0026amp; Dirmeyer, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such discrepancies between model predictions and observational data underscore the substantial uncertainties in how clouds and convection are represented in climate models (Bony et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schneider et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and highlight the complex, scale-dependent nature of vegetation-cloud interactions.\u003c/p\u003e \u003cp\u003eSeveral mechanisms have been proposed to explain the varying effects of trees on cloud cover. On the one hand, differences in surface properties induced by tree cover create spatial variations in energy availability and the partitioning between sensible and latent heat fluxes, influencing the evolution of the ABL and convective clouds development (Betts, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Heiblum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). On the other hand, roughness, largely related to tree cover heterogeneity, has also been identified as an important factor in controlling not only the surface energy balance and boundary-layer state (Harman, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) but also leading to enhanced turbulence and frictional convergence by slowing air masses (Rieck et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Teuling et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, the differing heating rates between vegetated and non-vegetated areas can create sea-breeze-like secondary circulations, resulting in mesoscale wind convergence that supports cloud development (Garcia-Carreras et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, soil moisture heterogeneity strongly influences convective cloud and rainfall patterns, particularly in the Sahel and other semi-arid tropical regions (Taylor et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, both tree cover and heterogeneity can be expected to significantly impact cloud formation. However, observational evidence confirming the influence of land surface heterogeneity on clouds remains limited, and the combined effects of these mechanisms are not fully understood. Additionally, much of the current research focuses on forested versus non-forested areas or the impacts of afforestation and deforestation, often overlooking the role of trees outside forests in cloud formation, which warrants further investigation.\u003c/p\u003e \u003cp\u003eIn this study, we aim to assess the impact of tree distribution on cloud formation across Africa, considering both the effects of average tree cover and spatial heterogeneity, as well as their confounding interactions. We use high-resolution tree cover (TC) maps from Reiner et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which map both forest and non-forest tree cover for continental Africa, allowing us to account for the full impact of all trees rather than just forests. Furthermore, using tree cover directly avoids the issue of inconsistent forest definitions (Zalles et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which can significantly affect the reliability of results. To capture the heterogeneity of tree cover distribution, we use Rao\u0026rsquo;s Q index (Rocchini et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which indicates the potential for similar tree density around a given pixel. Based on the details of tree cover distribution, we apply a \"space-for-time\" substitution method over a moving window (Li et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Duveiller et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to derive the sensitivity of cloud fraction cover (CFC) to both tree cover and spatial heterogeneity at different times of the day, relying on CFC data obtained from the geostationary satellites of the Meteosat Second Generation (MSG) program. Using this method, we minimize the effect of natural climate variability, focusing solely on the impacts of local vegetation contrasts. This enables us to provide an observational assessment of the impact of tree cover on cloud formation, eliminating the confounding effects of heterogeneity across Africa and offering an estimation of local cloud cover changes under different plausible future tree-planting scenarios that consider both tree quantity and spatial distribution. Additionally, we analyze the response of surface properties\u0026mdash;including sensible heat flux (SHF), latent heat flux (LHF), and land surface temperature (LST)\u0026mdash;to tree cover changes, comparing the mean state differences that drive cloud formation and exploring the potential mechanisms for vegetation-cloud interactions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBiophysical sensitivity of cloud cover to tree cover\u003c/h2\u003e \u003cp\u003eAs detailed in the ‘Methods’ section, we conducted a multivariable regression to derive the regression coefficient for tree cover (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e), Rao’s Q (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{Q}\\)\u003c/span\u003e\u003c/span\u003e), and their interaction term (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC,\\:\\:Q}\\)\u003c/span\u003e\u003c/span\u003e). The coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e indicates the biophysical sensitivity of cloud cover to tree cover under average Rao’s Q for each tree cover bin, with the effects of heterogeneity eliminated. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the annual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e values for daytime (13:00–15:00, local time) and nighttime (4:00–6:00, local time) cloud cover, indicating the potential annual mean cloud response to one standard deviation (29.3%) change in tree cover. A comparison of the average regression coefficients is further provided in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The use of geostationary satellite data enables calculations at an hourly resolution, and the selected hours here are expected to capture maximum daytime convection and maximum nighttime stability. We also calculated the sensitivity for full daytime (6:00–18:00, local time) and nighttime (18:00–6:00, local time) averages presented in Fig. S2. While similar spatial patterns emerge in the full-period averages, the analyses focused on specific hours reveal stronger signals. A positive \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e indicates that cloud presence increases with more tree cover and a negative value indicates an opposite direction of change. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, approximately 61.1% of the vegetated area shows a positive sensitivity of daytime cloud cover to tree cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). However, for nighttime cloud cover, the proportions of positive and negative effects are similar, with 53.5% of locations showing positive effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Spatially, strong daytime cloud enhancement occurs primarily near the equator in the central Congo basin and the marginal belts near the Sahara and Kalahari deserts. However, some areas at the boundary of the Congo Basin and the Chad Basin exhibit cloud reduction. At night, the impacts in the rainforest region are quite small, and the cloud cover is more responsive to increased tree cover in the arid regions, particularly near desert margins, with extensive negative effects observed in the plateau region in southern Africa around 10°S. Overall, the strength of cloud enhancement varies with latitude during the daytime, showing a latitude-averaged pattern of increased cloud cover with higher tree cover. In contrast, nighttime cloud effects exhibit more variable patterns of cloud enhancement and reduction across different latitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This latitude-dependent variability underscores the complexity of cloud formation processes and their sensitivity to tree cover, influenced by both diurnal and regional climatic conditions.\u003c/p\u003e \u003cp\u003eThe coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e also exhibits strong seasonality. In equatorial regions, daytime sensitivity is generally positive, peaking from September to December (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). In contrast, 5°S to 15°S shows season-dependent variability, with positive daytime cloud sensitivity dominant from September to November and negative sensitivity from March to May. This variation is largely driven by contrasting patterns in the plateau region of southern Africa, including the Lunda Plateau, Katanga Plateau, and Bié Plateau, etc, a trend that may characterize higher-latitude regions. At night, strong negative sensitivity is observed between 10°S and 15°S from June to September (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Additionally, equatorial regions experience cloud reduction effects, particularly from December to February. The marginal belts near the desert, between 10°N and 15°N, display varying cloud effects across different months. These seasonal contrasts in cloud cover and the differing responses between day and night highlight the need for further research to understand the underlying mechanisms driving these variations, particularly in regions with diverse climatic and geographical features.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e values across various climate conditions and topographical situations during the wet season (the wettest 3 months of the year) and dry season (the driest 3 months of the year), both calculated for each pixel individually. In tropical rainforest regions, primarily located in central Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), daytime and nighttime sensitivities exhibit opposite trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This contrast further suggests that vegetation differences drive cloud variability, as cloud effects diverge across day and night despite similar climatic conditions. For the magnitude, the daytime cloud enhancement effect is more than 3 times stronger than the nighttime reduction. Additionally, daytime sensitivity is more pronounced during the wet season than in the dry season, while at night, the pattern reverses. This seasonal difference is even marked in tropical monsoon regions. In tropical savannahs, the daytime vegetation-cloud effect is smaller than in tropical rainforest regions. Also, nighttime cloud effects in savannahs show seasonal contrasts, indicating more pronounced seasonal variability compared to rainforests. In summary, the analysis reveals distinct variations in cloud sensitivity across climatic zones and seasons, underscoring the complexity of climate interactions in different geographical contexts.\u003c/p\u003e \u003cp\u003eIt is important to note that the estimated cloud effects of forests could be modified by orographic conditions, given the dual influences of topography on both tree distribution and cloud formation. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec shows the distribution of elevation in Africa and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed illustrates the average \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e across seven elevation categories ranging from 0 to 3,000 m. Notably, higher elevations generally exhibit more pronounced positive sensitivity to tree cover during the wet season, with daytime values exceeding nighttime values. During the dry season, daytime cloud sensitivity remains positive but is relatively consistent across elevation categories, except for elevated areas between 500–750 m and above 1,500 m. For nighttime clouds, negative sensitivity dominates and decreases with elevation, particularly in regions above 2,000 m. These high-elevation variations help explain the seasonally diverse cloud patterns observed in the plateau region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee), which indicate a strong suppressive effect of tree cover on nighttime cloud formation at high elevations under water-limited conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePotential mechanisms connecting cloud sensitivity and tree cover\u003c/h3\u003e\n\u003cp\u003eWhile various biophysical processes are involved in the interactions between trees and clouds, it remains unclear which factors determine the spatial patterns of cloud enhancement and reduction during day and night. In terms of biophysical differences, regions with dense tree coverage generally exhibit lower albedo, higher surface roughness, lower land surface temperature (LST), and increased evapotranspiration compared to short-vegetation systems (Li et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Duveiller et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, these differences may impact the cloud in divergent ways under different conditions.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the sensitivity of daytime sensible heat flux (SHF) and nighttime land surface temperature (LST) to changes in tree cover (i.e.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\beta\\:}_{TC}\\:\\)\u003c/span\u003e\u003c/span\u003efor SHF and LST), with the sensitivities for other variables presented in Fig. S3 and Fig. S4. The LST data used in the study is the all-sky data derived from clear-sky LST with cloudy pixels filled by the skin temperature from the energy balance model, therefore the LST under the cloud can also be analyzed. Increased tree cover across Africa generally leads to higher daytime latent heat flux (LHF) during both wet and dry seasons, suggesting greater moisture availability for cloud formation (Fig. S3c and Fig. S3g), while daytime LST consistently decreases due to the cooling effects of trees (Fig. S3d and Fig. S3h). However, SHF exhibits distinct patterns: it increases with more tree cover in central forest regions but decreases in savannahs surrounding rainforests; in arid regions, SHF sensitivity shows positive values during the dry season but reverses in the wet season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This pattern arises because greening typically results in a greater increase in LHF in warm and dry regions due to increased evaporative surfaces, causing SHF to change in the opposite direction (Forzieri et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In these regions, despite potential soil moisture limitations, increased tree cover enhances LHF through complex tree adaptations, such as root development and groundwater access (Fan \u0026amp; Miguez-Macho, 2013). Moreover, the opposite SHF trends in arid regions near deserts during the wet and dry seasons are primarily driven by moisture supply, which regulates transpiration. Existing studies confirmed that SHF variations largely influence cloud formation (Bosman et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef display the spatial distribution of the direction of daytime \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e for CFC and SHF, where point colors denote the relationship between these two variables, and point size reflects the magnitude of cloud sensitivity. In the wet season, positive synchrony (green points) is prominent in rainforest regions, constituting 24.3% of pixels. Negative synchrony (blue points, 23.0%) appears mainly in tropical savannahs surrounding forests and parts of the temperate zone. Cloud formation requires both a lifting mechanism (from SHF) and sufficient moisture (from LHF). Therefore, in these relatively humid regions, greater SHF fosters turbulent mixing and boundary layer development (Fisch et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), promoting upward air motion and cloud formation (Gentine et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bosman et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, opposing CFC and SHF trends (red points, 36.2%) are prominent in arid regions and part of temperate zones, extending even to tropical savannahs in southern Africa. In these water-limited regions, SHF is already adequate to induce the lifting of local moisture, and tree cover primarily enhances LHF, providing extra moisture and supporting cloud formation even though SHF decreases. In dry seasons, the vegetation-cloud effect is strong in central Africa, with positive synchrony remaining prevalent in tropical rainforests. However, surrounding savannahs exhibit both yellow and blue dots, suggesting that while SHF sensitivity varies, cloud reduction persists, which implies the moisture limitation in savannas in the dry season.\u003c/p\u003e \u003cp\u003eAt nighttime, LHF still increases across Africa with more tree cover, albeit to a smaller extent (Fig. S4c and Fig. S4g). In contrast, nighttime SHF mainly decreases due to the lack of incoming shortwave radiation at night (Fig. S4b and Fig. S4f), making the impacts of surface energy fluxes generally negligible under these conditions. However, nighttime LST exhibits distinctive patterns. Nighttime LST increases with more tree cover across the tropical rainforest regions, especially during the dry season. Forests, with their high surface roughness from taller canopies, promote greater mixing and heat dissipation during the day but can act as a heat \"trap\" at night (Lee et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As a result, central forests can better maintain warmer surface temperatures at night when tree cover increases, particularly when transpiration is restricted in the dry season. In less humid regions, including tropical savannahs, arid steppes, and temperate zones, nighttime LST shows a negative relationship with TC in the dry season, while a positive sensitivity is observed in the wet season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). In the wet season, soil with higher moisture content has a greater heat capacity, allowing more daytime heat storage (Peng et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). More trees contribute to heat aggregation processes in the daytime, slowing the nighttime temperature decline. The spatial distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\)\u003c/span\u003e\u003c/span\u003e for nighttime CFC and LST shows related patterns, especially during the dry season. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh illustrate the direction of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\:\\)\u003c/span\u003e\u003c/span\u003efor nighttime CFC and LST. In the dry season, substantial cloud effects appear, with orange and red points—indicating an inverse relationship between nighttime LST and CFC sensitivity—dominating at 39.7% and 24.0%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). This relationship suggests that cloud formation is more likely when nighttime LST decreases with increased tree cover, whereas cloud reduction occurs when LST increases at night. Cooler nighttime temperatures lead to nocturnal temperature inversion and higher relative humidity, increasing the likelihood of water vapor condensing into droplets and forming clouds when sufficient moisture is present (Dommo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Babić et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Conversely, nighttime clouds can warm the surface by enhancing downward longwave radiation (Dai et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This warming effect may be the dominant process in regions where LST sensitivity aligns with CFC sensitivity during the dry season. In the wet season, the nighttime vegetation-cloud effect is significant only in arid regions. Increased tree cover drives cloud formation regardless of whether the land surface cools or warms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), suggesting that the impact of LST diminishes under sufficient water availability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEstimation of cloud change under different tree cover change conditions\u003c/h3\u003e\n\u003cp\u003eIn the previous section, we analyzed the individual impacts of tree cover change on cloud formation. However, changes in tree cover are typically accompanied by alterations in tree cover heterogeneity, which also influence cloud formation. Therefore, we calculated changes in cloud fraction cover (CFC) under specific modifications in average tree cover and Rao’s Q to assess the combined roles of tree cover and heterogeneity in cloud formation. In this analysis, average tree cover was assumed to change by 20% of the maximum tree cover on the map, while Rao’s Q was adjusted by 20% of the maximum value within each 1% tree cover bin. Pixels exceeding the maximum or falling below the minimum limits were adjusted accordingly. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays daytime cloud cover changes across Africa under varying tree cover and Rao’s Q scenarios. For the original Rao’s Q, a 20% decrease in tree cover primarily reduces cloud cover, with localized increases in savannahs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). When heterogeneity is reduced simultaneously, the cloud reduction becomes more pronounced, especially in arid steppe regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Conversely, increasing heterogeneity strengthens cloud inhibition in tropical rainforests and enhances cloud cover in savannahs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). A 20% increase in tree cover significantly enhances cloud cover in arid and temperate zones but reduces it in some savannahs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Decreased heterogeneity amplifies this reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), whereas increased heterogeneity heightens cloud sensitivity to tree cover changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei). The center column illustrates the scenario where heterogeneity changes while tree cover remains constant, representing a hypothetical rearrangement of trees into either more compact, structured configurations or more randomly distributed patterns. In tropical rainforests and surrounding densely vegetated areas, greater heterogeneity is associated with reduced cloud cover, whereas in savannahs and arid steppe regions, increased heterogeneity enhances cloud formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). These findings reveal distinct regional variations in how tree cover and heterogeneity influences cloud formation, which may be influenced by background climate, particularly the baseline percentage of tree cover within each region or pixel. In areas with already high forest density, even minor changes could significantly disrupt local microclimates and affect cloud formation. Although findings from Wang et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) indicate that increased heterogeneity in deforested regions contributes to shallow cloud formation in the Amazon, it remains uncertain whether this effect will persist under deforestation at larger scales. On average, we observe a 55.2% increase in the cloud enhancement effect when tree cover and heterogeneity are considered together, compared to the effect of tree cover increase alone in tropical savanna regions, and this value is 12.4% in arid steppe zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej). Increased heterogeneity enhances surface roughness, contributing to turbulence and frictional convergence. Additionally, the contrast between trees and non-trees influences the formation of mesoscale circulation, further enhancing cloud formation. Generally, heterogeneity’s influence on cloud formation shifts with average tree cover: it positively impacts clouds at low tree cover but turns negative as cover increases. These variations highlight the intricate relationship between tree cover, heterogeneity, and cloud formation, underscoring the importance of considering both tree numbers and spatial configuration in the context of planting trees.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe calculation is also applied for nighttime cloud cover (Fig. S5). Under the original tree cover conditions, increased heterogeneity generally promotes cloud formation at night, except for high-elevation regions (Fig. S5h), while decreased heterogeneity typically reduces cloud formation (Fig. S5b). When tree cover decreases, positive cloud effects are observed in high-elevation regions of southern Africa, while other regions exhibit negative effects (Fig. S5d). Further reductions in heterogeneity will amplify this pattern (Fig. S5a). Conversely, an increase in Rao’s Q under these conditions significantly shifts cloud cover dynamics, resulting in positive changes across most regions (Fig. S5g). For tree cover increases, arid regions remain the most sensitive, experiencing substantial increases in cloud cover. However, high-elevation areas in southern Africa again exhibit decreases in cloud cover (Fig. S5f). Additional heterogeneity further enhances cloud increases in arid regions (Fig. S5i), while reduced heterogeneity expands areas with negative cloud cover effects, even with increased tree cover (Fig. S5c). Since nighttime cloud cover is closely linked to LST, the distinct temperature feedbacks in high-elevation regions may explain their differing responses to changes in heterogeneity.\u003c/p\u003e\n\n "},{"header":"Discussion and perspectives","content":"\u003cp\u003eIn this study, we used a space-for-time approach to investigate local cloud effects of tree cover distribution change in Africa, incorporating both absolute coverage and spatial configuration, which advances our understanding of vegetation–cloud interactions by revealing key patterns across various regions. As for the individual impacts of increased tree cover, our findings show that increased daytime cloud cover occurs over the tropical rainforest and the arid steppe regions, whereas a reduction in cloud formation is observed over tropical savannahs. At night, a stronger negative relationship between tree cover and cloud formation is observed during the dry season, particularly in the high-elevation regions of southern Africa. Mechanistically, the spatial variation in cloud formation is linked to sensible heating during the day in regions where water is sufficient. In water-limited regions, however, cloud formation responds differently as moisture availability becomes the controlling factor. At night, cloud effects are more related to land surface temperature differences induced by tree cover, likely due to water condensation on cooler surfaces. When considering spatial configuration, cloud predictions under different scenarios indicate that greater heterogeneity enhances cloud formation in savannah and arid steppe regions as tree cover increases. Conversely, in tropical rainforest regions, increased heterogeneity amplifies the reduction in cloud cover caused by declining tree cover. This finding implies that when planning tree restoration in savannah regions of Africa, trees should be distributed in more random, heterogeneous arrangements to maximize cloud cover. The corollary to this suggested by our results is that deforestation in tropical forest regions would lead to a greater reduction in cloud cover if it occurs in a sparse manner, akin to what would happen following selective logging, rather than equivalent reductions in forest cover concentrated in clear cuts. Future monitoring should consider varying conditions across the degradation-deforestation continuum (Lapola et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as these differences can also significantly impact atmospheric processes. These insights highlight that the method of afforestation or deforestation is as important as the location of tree planting, providing valuable guidance for the planning and implementation of future tree restoration projects in Africa.\u003c/p\u003e\u003cp\u003eThis study provides continent-scale observational evidence of cloud sensitivity to tree cover and enhances our mechanistic understanding of vegetation-cloud interactions. The cloud effects estimated in our study reflect the local impact of tree cover on cloud formation, offering a more realistic representation of fine-scale changes in tree cover. By incorporating surface heterogeneity, we also highlight the role of small-scale turbulence, frictional convergence, and mesoscale circulation—factors that are typically unresolved in global climate models. While cloud processes are inherently complex, and the cloud cover observations used in this study offer only a simplified view, our analysis serves as an approximation of vegetation-cloud interactions and can act as a reference for model simulations. This can help constrain and calibrate models, thereby improving their ability to derive a more nuanced understanding of the mechanisms behind the consequences of changes in the biophysical properties of land.\u003c/p\u003e\u003cp\u003eSeveral uncertainties must be considered. A key source of uncertainty is the cloud data. We utilized full cloud fraction cover data, which includes low, medium, and high-level clouds. Low-level clouds, which form within the boundary layer, are considered to be more likely to exhibit a strong spatial correlation with the underlying surface (Duveiller et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Considering all cloud types together may introduce random noise from medium- and high-level clouds, which are not directly related to vegetation patterns. However, high-level deep convection clouds are also expected to be influenced by the surface (Xu et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which should not be neglected. Additionally, labeling cloud types in total cloud cover maps based on cloud classification schemes adds further uncertainty, as most cloud types identified by ISCCP (International Satellite Cloud Climatology Project) joint histograms (Rossow \u0026amp; Schiffer, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) do not correspond to single-layered or uniquely defined geometric cloud types (Mace \u0026amp; Wrenn, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As a result, cloud classification complicates the analysis, making outcomes highly sensitive to the accuracy of cloud separation. Since our study focuses on periods when cumulus and stratocumulus clouds are at their peak (Eastman \u0026amp; Warren, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the results likely reflect surface influences. To further address this, the cloud profile retrievals from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat could be applied to better differentiate between various cloud phases and types. Additionally, our method captures only a fraction of the local effects of tree cover distribution on cloud formation. Processes such as advection may further obscure the signal (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), complicating efforts to isolate the contribution of tree cover changes to cloud dynamics. Therefore, case studies with field measurements could serve as useful validation and complement existing results. Moreover, the observational nature of this study limits our ability to fully disentangle the various confounding factors that drive cloud formation. The complexity of cloud formation processes—such as boundary layer turbulence, mesoscale circulation, and frictional convergence—makes it challenging to identify the precise mechanisms involved (Teuling et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Lastly, the study area for this paper is limited to Africa, with data from the year 2019, constrained by the availability of high-resolution tree maps and geostationary satellite cloud data. In the future, multi-year studies could be conducted across multiple regions globally, such as the United States, China, and Europe, where geostationary satellite data is available. Such explorations will provide a more comprehensive assessment of how tree cover patterns influence cloud formation.\u003c/p\u003e\u003cp\u003eAs an extension, the tight coupling between cloud and precipitation processes suggests that changes in tree cover could significantly impact precipitation patterns (Roy, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Garcia-Carreras \u0026amp; Parker, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hartley et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Directly observing the effects of deforestation on precipitation is challenging. However, high-resolution satellite data on cloud impacts can provide valuable insights into potential precipitation changes, particularly in tropical regions dominated by convective rainfall. Although our analysis does not directly relate cloud formation to rainfall, we can anticipate that the cloud formation we see could strengthen the hydrological value of vegetation. Additionally, changes in tree cover due to deforestation or afforestation affect not only local climate and hydrology but also have remote impacts on precipitation, runoff, and water availability in distant regions through mechanisms like moisture recycling and advection (Van Der Ent et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wang-Erlandsson et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hoek Van Dijke et al., 2022). Accurate predictions of these impacts require a better understanding of vegetation–cloud interactions. To explore the full picture, including both local and nonlocal effects, the only option is to integrate experimental evidence with the formal representation of processes in Earth system model experiments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInput data and pre-processing\u003c/h2\u003e \u003cp\u003eThe primary data for this study originate from distinct satellite remote sensing products. For Cloud Fraction Cover (CFC), we use the CLAAS-3 dataset, derived from Spinning Enhanced Visible and InfraRed Imager (SEVIRI) observations onboard the Meteosat Second Generation (MSG) geostationary satellites, operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) (Meirink et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This dataset includes 15-minute cloud detection data over a fixed disk covering Europe, Africa, and Eastern South America, with a spatial resolution that ranges from 3 km at nadir up to approximately 5 km at the disk edge (Benas et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The probabilistic cloud mask retrieved using a naive Bayesian approximation from level 2 data is used here. Additionally, half-hourly datasets for Latent Heat Flux (LHF), Sensible Heat Flux (SHF), and Land Surface Temperature (LST) from MSG geostationary satellites (Trigo et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with a resolution of 0.05\u0026deg;, are used to assess surface processes related to cloud formation. The LHF and SHF data are derived by solving the surface energy balance at the tile level, following the TESSEL (Tiled ECMWF Scheme of Surface Exchange over Land) model (Van den Hurk et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These datasets of surface energy fluxes have demonstrated good coherence with eddy covariance data and other global/continental-scale products (Barrios et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For LST, we use the all-sky LST, which combines clear-sky LST retrieved from MSG infrared measurements with LST estimated via the same land surface energy balance model used to derive surface energy fluxes. This method fills gaps caused by cloud cover and provides accurate LST estimates compared to in-situ LST obtained under all-sky conditions (Martins et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The original datasets were processed into monthly averages of hourly values at a 0.05\u0026deg; resolution for the year 2019. The original MSG cloud data in Coordinated Universal Time (UTC) were adjusted to local time before analysis.\u003c/p\u003e \u003cp\u003eFor Tree Cover (TC), the high-resolution tree cover dataset for Africa in 2019, provided by Reiner et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), is used to calculate the average TC condition and to assess spatial heterogeneity with a 0.05\u0026deg; spatial resolution. This dataset is based on 3 m spatial resolution satellite imagery from Planet Labs and uses deep learning techniques to accurately map tree cover down to individual trees. In this study, we used the map aggregated to a spatial resolution of 100 m and considered areas on the African continent where tree coverage exists. As a result, the desert regions and open water areas were excluded.\u003c/p\u003e \u003cp\u003eTo analyze the impacts of climate conditions on cloud-vegetation sensitivity, we adopted the K\u0026ouml;ppen-Geiger climate classification (Peel et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and classified the African continent into eight climatic zones (i.e., tropical rainforest, tropical monsoon, tropical savannah, arid desert, arid steppe, temperate zone, cold zone, and polar zone) according to the climate classification map from Beck et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To filter out the impact of altitude on the local cloud and discuss the topological impacts on cloud-vegetation sensitivity, we used a digital elevation model (DEM) with a 0.05\u0026deg; spatial resolution, aggregated from the version 4 Shuttle Radar Topography Mission (SRTM) with a 90 m resolution.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCalculation of tree cover heterogeneity\u003c/h3\u003e\n\u003cp\u003eUsing the 100-meter tree cover map, we apply the entropy-based Rao\u0026rsquo;s Q index (Q) to quantify the tree cover heterogeneity. Entropy-based indices are commonly employed to evaluate the heterogeneity and complexity of spatially arranged data in ecological studies (Parrott, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Altieri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cushman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). When translating such methods from species counts to pixel values, some changes are warranted. The widely-used classical Shannon entropy index (Shannon, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1948\u003c/span\u003e), accounts for richness and relative abundance of classes of spectral values but does not explicitly consider the numerical magnitude (values) of pixels. On the contrary, Rao\u0026rsquo;s Q index takes into account the value of pixels by considering their pairwise differences, here considered as a distance (Rocchini et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For each pixel, a surrounding window is selected to calculate Rao\u0026rsquo;s Q as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Q=\\:\\sum\\:\\sum\\:{d}_{ij}\\times\\:{p}_{i}\\times\\:{p}_{j}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis index is based on classes of TC at the scale of 100 m pixels. TC values (in percentage) are rounded to the nearest integer to assign their class, thus resulting in classes with a range of 1%. The subscripts \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e represent different classes of TC within the window. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e is defined as the ratio of pixels falling in the class representing value \u003cem\u003ei\u003c/em\u003e to the total number of pixels in the window; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the pairwise distance between the classes of value \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, calculated as the absolute value of the difference between \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eBased on its definition, Rao\u0026rsquo;s Q represents the expected difference in tree cover values between two pixels drawn randomly with replacement from the considered window, indicating the potential for similar tree density surrounding the target pixel. Therefore, a small value of Rao\u0026rsquo;s Q indicates homogeneous tree cover, while a high value indicates heterogeneity. In this study, we used a moving window of 5 \u0026times; 5 pixels (representing a region of 500 \u0026times; 500 m) for each pixel and generated the distribution map of Rao\u0026rsquo;s Q at a spatial resolution of 100 m. The spatial distribution of trees in Africa, along with examples of tree cover and Rao\u0026rsquo;s Q within a 0.05\u0026deg; \u0026times; 0.05\u0026deg; window, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In terms of tree cover, regions with high tree cover are primarily located in the forested areas of central Africa, while the surrounding savannah regions exhibit lower tree cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In contrast, Rao\u0026rsquo;s Q values are higher in the savanna regions and some temperate zones, where tree cover is lower compared to the Congo Basin, which displays a more uniform tree cover distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The sample regions A, B, C, and D, located in different climate zones, have average tree cover values of 82.89%, 46.13%, 32.56%, and 3.42%, respectively. Their corresponding average Rao\u0026rsquo;s Q values are 8.12%, 11.16%, 7.00%, and 1.40%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). This figure suggests that Rao\u0026rsquo;s Q varies with both average tree cover and distribution patterns, which together impact the cloud formation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRemoval of tree cover-heterogeneity relationship\u003c/h3\u003e\n\u003cp\u003eTo match the spatial resolution of the cloud cover data, the original tree cover and the derived Rao\u0026rsquo;s Q map, both in 100 m resolution, are further aggregated to 0.05\u0026deg;. Since the average tree cover and Rao\u0026rsquo;s Q both originate from the 100 m tree cover map, there is an inherent quadratic relationship between the two variables, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea. When the average tree cover approaches 0% or 100% coverage, Rao's Q approaches 0%, indicating homogeneity with either no trees or complete tree coverage. Conversely, when the average tree cover is between these extremes, particularly at 50%, Rao\u0026rsquo;s Q values are high and exhibit considerable variability. To isolate the individual impacts of tree cover and tree heterogeneity, we scale Rao\u0026rsquo;s Q using Z-score normalization within each 1% tree cover bin (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). This ensures that pixels share relatively comparable Rao\u0026rsquo;s Q values across different levels of tree cover. We use this normalized Rao\u0026rsquo;s Q as the input for further analysis, and the normalized distribution, along with the corresponding mean and standard deviation for each pixel, is shown in Fig. S6. To make sure that tree cover and Rao\u0026rsquo;s Q are comparable in magnitude, the tree cover map is also scaled using Z-score normalization with a mean of 18.4% and a standard deviation of 29.3%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of cloud sensitivity to tree distribution\u003c/h2\u003e \u003cp\u003eIn this study, we use a \"space-for-time\" method to calculate the biophysical sensitivity of clouds to average tree cover and spatial heterogeneity locally. This method is commonly applied to explore the local impacts of land use and land cover change on variables such as temperature and energy fluxes (Li et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Duveiller et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The core assumption is that the target pixel shares the same background climate as adjacent pixels within a moving window. Therefore, any differences observed between the target and contrasting pixels are attributed to the biophysical feedback of local land cover change (Lee et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, we assume that TC, Rao\u0026rsquo;s Q, and their interaction are the drivers of spatial variation in CFC under certain hydroclimate conditions when the elevation difference is controlled between pixels. This allows us to regress the biophysical sensitivity of clouds to tree distribution from spatially proximate observations. A key advantage of this method over temporal regression strategies is that it eliminates the influence of natural climate variability and long-term warming trends on vegetation growth, as pixels with varying TC and Rao\u0026rsquo;s Q values within the moving window are subject to the same background climate.\u003c/p\u003e \u003cp\u003eThe specific approach of this strategy works as follows: for each pixel from the TC, Rao\u0026rsquo;s Q and CFC map at a resolution of 0.05\u0026deg;, potential comparison samples are selected from spatially nearby pixels within a moving window, which is set to 9 \u0026times; 9 pixels (representing approximately a region of 50 \u0026times;50 km), based on previous studies (Xu et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To minimize the influence of topography, we only consider pixels where the elevation difference between the selected pixels and the target pixel is less than 100 meters. Using this method, we can determine the biophysical sensitivity for the target pixel by regressing the differences in TC, Rao\u0026rsquo;s Q, and CFC between all selected comparison pixels and the target pixel. The scaled tree cover and the scaled Rao\u0026rsquo;s Q map are used as the input. To account for the individual impacts of TC and Rao\u0026rsquo;s Q, as well as their confounding effects, the regression is conducted using multivariable linear regression with interaction terms:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CFC={\\beta\\:}_{0}+{\\beta\\:}_{TC}\\times\\:TC+{\\beta\\:}_{Q}\\times\\:Q+{\\beta\\:}_{TC,\\:Q}\\times\\:TC\\:\\times\\:Q+ϵ\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC}\\:,\\:{\\beta\\:}_{Q}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{TC,Q}\\:\\)\u003c/span\u003e\u003c/span\u003eare the coefficients for TC, Rao\u0026rsquo;s Q, and their interaction term; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ϵ\\)\u003c/span\u003e\u003c/span\u003e represents the random error. Additionally, we only calculate the sensitivity if there are at least 25% valid samples in each window and if the minimum TC difference is greater than 10%, ensuring the robustness of our results.\u003c/p\u003e \u003cp\u003eAlso, this \u0026ldquo;space-for-time\u0026rdquo; procedure is applied to sensible heat flux (SHF), latent heat flux (LHF), and land surface temperature (LST), together with tree cover to calculate the impacts of tree cover distribution on these variables. Using the spatial regression method described, we obtain the monthly biophysical sensitivity of CFC, LHF, SHF, and LST to TC at a 0.05\u0026deg; spatial resolution. To further exclude the impact from outliers, we remove the value within the maximum and the minimum 1% value based on the cumulative distribution frequency (CDF) for all the monthly results. Regressions are performed hourly for each month. Since cumulus clouds typically peak in the early afternoon and stratocumulus clouds form predominantly at the end of the night (Eastman \u0026amp; Warren, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), we focus on the hours from 13:00 to 15:00 to represent daytime sensitivity, as these hours are primarily influenced by convective clouds. Similarly, the hours from 04:00 to 06:00 are analyzed to capture nighttime sensitivity, when atmospheric conditions are most stable. This ensures that the detected signal can largely be attributed to vegetation properties on land.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal analysis\u003c/h2\u003e \u003cp\u003eIn addition to calculating the annual and monthly sensitivities, we also calculated changes for the dry season (the driest 3 months of the year), and the wet season (the wettest 3 months of the year). The driest and wettest months were identified for each pixel using the individual precipitation data from the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) Version 2 dataset (Funk et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) with a spatial resolution of 0.05\u0026deg;.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe CLAAS-3 cloud dataset used in the study are available on EUMETSAT Climate Monitoring Satellite Application Facility program at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wui.cmsaf.eu/safira/action/viewHome\u003c/span\u003e\u003c/span\u003e. The sensible heat flux, latent heat flux and land surface temperature are obtained on EUMETSAT Land Surface Analysis Satellite Application Facility program at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lsa-saf.eumetsat.int/en/\u003c/span\u003e\u003c/span\u003e. The Africa tree cover data from Reiner et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) are available for download at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.7764460\u003c/span\u003e\u003c/span\u003e. The SRTM DEM is available on the Google Earth Engine at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/earth-engine/datasets/catalog/CGIAR_SRTM90_V4\u003c/span\u003e\u003c/span\u003e. The K\u0026ouml;ppen-Geiger climate classification map is accessible via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.gloh2o.org/koppen\u003c/span\u003e\u003c/span\u003e. The CHIRPS Version 2 dataset are freely available to download from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.chc.ucsb.edu/products/?C=M;O=D\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eD.X. led the conceptualization, methodology development, investigation, visualization, and writing of the original draft, as well as contributed to the review and editing. L.C. assisted with methodology development and contributed to the review and editing. M.R. played a role in reviewing and editing the manuscript and contributed to funding acquisition. D.Z. provided supervision and contributed to funding acquisition. G.D. played a central role in the conceptualization, methodology development, supervision, and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research was supported by the Topology of Hydrosphere Project by Key Laboratory of Hydrosphere Sciences of the Chinese Ministry of Water Resources (grant no. sklhse-TD-2024-F01, DZ). This research resulted from a research stay of D.X. in G.D.\u0026rsquo;s research group. This stay was supported by China Scholarship Council as No. 202306210299. 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Communications Earth \u0026amp; Environment, \u003cem\u003e5\u003c/em\u003e(1), 620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43247-024-01779-9\u003c/span\u003e\u003cspan address=\"10.1038/s43247-024-01779-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5639740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5639740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVegetation plays a pivotal role in regulating climate and sustaining the hydrological cycle, with both the quantity and distribution of trees influencing surface and atmospheric processes. While the direct effects of vegetation on surface properties are well-documented, the indirect impacts of trees on clouds\u0026mdash;especially those from trees outside the forest\u0026mdash;are less explored, with spatial tree distribution often neglected. This study examines how tree cover, in terms of absolute coverage and spatial configuration, affects cloud formation over Africa. Our findings reveal distinct patterns of cloud sensitivity to tree cover changes across climatic zones and elevations, linked to energy partitioning during the day and land surface temperature disparities at night. Additionally, combining increases in tree cover and heterogeneity enhances cloud formation by 55.2% in tropical savannas compared to tree cover increase alone, underscoring the importance of strategic tree placement. This data-driven analysis enhances the understanding of vegetation-cloud interactions and provides valuable insights for tree restoration projects in Africa.\u003c/p\u003e","manuscriptTitle":"Beyond Canopy Cover: How Tree Distribution Shapes Cloud Formation Across Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 09:45:42","doi":"10.21203/rs.3.rs-5639740/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ecfd984b-25a6-4c55-aec2-f72327587df4","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":41889829,"name":"Earth and environmental sciences/Ecology/Ecosystem ecology"},{"id":41889830,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":41889831,"name":"Biological sciences/Ecology/Forest ecology"}],"tags":[],"updatedAt":"2025-12-17T20:40:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-22 09:45:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5639740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5639740","identity":"rs-5639740","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}