Deforestation amplifies climate change effects on warming and cloud level rise in African montane forests

Domain: earth_science
Published: 2024-08-14
Editorial outcome: major_revision

Abstract
Tropical montane forest ecosystems are pivotal for sustaining biodiversity and essential terrestrial ecosystem services, including the provision of high-quality freshwater. Nonetheless, the impact of montane deforestation and climate change on the capacity of forests to deliver ecosystem services is yet to be fully understood. In this study, we offer observational evidence demonstrating the response of air temperature and cloud base height to deforestation in African montane forests over the last two decades. Our findings reveal that approximately 18% (7.4 ± 0.5 million hectares) of Africa’s montane forests were lost between 2003 and 2022. This deforestation has led to a notable increase in maximum air temperature (1.37 ± 0.58 °C) and cloud base height (236 ± 87 metres), surpassing shifts attributed solely to climate change. Our results call for urgent attention to montane deforestation, as it poses serious threats to biodiversity, water supply, and ecosystem services in the tropics.

Introduction
Tropical montane forests are characterized by a unique hydroclimate and rich biodiversity, providing essential ecosystem services to humans and hosting a wide variety of endemic plants and animals1–4. Montane forests are the interface for the transfer of water from the atmosphere to the land, providing a stable freshwater supply to the surrounding lowlands4,5. Tree cover plays a key role in maintaining a sustainable water supply by intercepting additional water from clouds and fog through tree leaves and by replenishing the atmospheric water content through moisture recycling6–9. Tree cover cools local daytime temperatures through shading, absorption of incoming solar radiation, and evapotranspiration, which helps in maintaining a lower cloud base height for cloud water interception8,10.

Tropical montane forests in Africa are highly threatened by deforestation and climate change, posing a serious risk to the sustainability of water supply in the region7,11–13. Previous studies have reported the impact of lowland deforestation on the cloud regime of Monteverde forest in Costa Rica11,14. An increase in cloud base height (CBH) impacts the capacity of mountain ecosystems to provide water supply by reducing the forest area under cloud cover11, the frequency of cloud immersion, and the quantity of cloud water intercepted by trees6,8,15. However, the effect of montane deforestation on CBH and the sustainability of water supply in Africa remain unknown. It is unclear to what extent and magnitude the CBH has shifted in response to deforestation, and whether geographical hotspots exist that require special attention. Furthermore, whether deforestation accelerates the impacts of climate change on CBH across montane forests in Africa remains poorly understood.

In recent decades, with an increase in the availability of satellite observation data, spatially explicit information on the global extent of deforestation and its impact on local temperature is improving16–19. Consequently, a growing body of literature has studied the impacts of deforestation on local temperature using satellite observations, following either a time series16,18–21 or a space-for-time substitution17,22,23 approach. These studies are consistent in their findings that deforestation in the tropics leads to greater surface temperature warming (~3 °C maximum) than elsewhere. Nonetheless, the focus has been mainly on radiometric surface temperature17–19,21, rather than air temperature, and in showing how deforestation impacts vary globally across latitudes. Furthermore, studies using the space-for-time substitution approach have largely excluded montane forests, as this approach is less suitable for areas with large topographic gradients17,22. Hence, an exclusive study on the impacts of montane deforestation on CBH and air temperature warming is lacking.

With recent in situ measurements providing evidence of CBH increases in response to canopy removal in tropical montane forests8, it is crucial to investigate how the impacts of montane deforestation on CBH vary spatially in Africa. In particular, while montane deforestation is accelerating globally (at an annual rate of 0.31%), tropical montane forests in Africa have experienced the highest rate of deforestation in the last two decades (0.48% per year)24. Although controversial and geographically variable, local rates of montane deforestation in unprotected areas of Africa can be as high as 3% per year25. The main driver of montane deforestation in Africa has been attributed to small-scale cropland expansion, with other factors (e.g., urbanisation, large-scale commodity crops and forest fires) playing a lesser role26,27.

Here, we investigated the impacts of deforestation in montane forests in Africa over the past two decades with the objective of evaluating and providing 1) spatially explicit information on deforestation-induced maximum air temperature and CBH change, and 2) comparing the relative and net impacts of deforestation-induced and climate change-induced CBH change. The study followed a data-driven approach consisting of three steps (Methods). First, tree cover loss in montane forests, between 2003 and 2022, was identified using the Global Forest Change (GFC) product, and a forest gain filtering procedure, based on a spectral mixture model, was applied to exclude pixels that have recovered after forest loss. Second, air temperature and dew point temperature at a 1-km resolution were estimated by building an ensemble learning model based on satellite observation data, which were trained and validated using 498 weather station data over Africa. The impact of deforestation on air temperature between 2003 and 2022 was estimated using a time series approach after filtering and removing the impact of the concurrent background climate change signal following previous studies16. While the deforestation impact on CBH was estimated using an empirical method based on air temperature and dew point temperature28 (Methods), the climate change impact on CBH was studied using 30 years of data from ERA5-Land for the baseline period from 1992 to 2022.

We show that tree cover loss in tropical montane forests increases air temperature and CBH, yet the magnitude is controlled by the opposing effect between steep elevation gradients and the extent of tree cover loss. A large tree cover fraction loss in a montane forest can offset the cooling effect of elevation gradients. We further show that deforestation has a stronger impact on the magnitude of CBH than climate change does.

Results
Widespread forest loss in the lower montane forests across Africa
Between 2003 and 2022, montane forests decreased by 18% with 7.4 ± 0.5 million ha of forest lost during this period in Africa (Fig. 1a). When evaluated on a 1 km × 1 km grid, approximately 80% of the forest loss occurred at an elevation ≤ 1800 m above sea level (a.s.l.), with an average fractional tree cover loss of 15% (Fig. 1b, c). Montane forests that have lost ≥ 50% of their initial tree cover in 2003 represent only 3.6% of the total forest loss. The number of areas affected by tree cover loss decreases with increasing elevation, whereas the intensity of deforestation (i.e., fraction of tree cover loss) tends to increase with elevation (Fig. 1c, d). Hence, over the last two decades, forest loss in the montane areas of Africa has occurred mainly at lower elevations and in smaller patches.

Forest loss increased the air temperature and CBH to varying degrees across Africa between 2003 and 2022 (Fig. 2). The mean annual average maximum temperature (Tmax) increased by 1.37 ± 0.58 °C and the CBH by 236 ± 87 m. Locally, Tmax increased by a maximum of 3 °C and the CBH by a maximum of 300 m, with peaks in both variables occurring between 5° S and 10° S latitude. To the contrary, forest loss decreased the dew point temperature by a comparable magnitude of −1.42 ± 0.55 °C (refer to Supplementary Fig. 1 for details of the deforestation-induced dew point temperature change). Additional information regarding the seasonality of deforestation-induced Tmax and CBH change is provided in Supplementary Fig. 2a, b.

Elevation and tree loss control magnitude of warming and CBH increase
The influence of elevation and tree cover change on the magnitude of warming and CBH increase is shown in Fig. 3a–c. For comparison purposes, the effect on maximum land surface temperature (LST) is included in the analysis. At lower elevations (1200–1600 m a.s.l.), all variables (LST, Tmax, and CBH) increase with the fraction of tree cover loss until a steep elevation gradient reduces the magnitude of warming and CBH increase despite an increase in the fraction of tree cover loss. However, when the fraction of tree cover loss reaches a certain threshold (i.e., 40% for LST, 44% for Tmax, and 42% for CBH), this trajectory is reversed and the warming and CBH increase steeply regardless of elevation as these thresholds were enough to start offsetting the elevation effect (Fig. 3a–c). The result shows that the contrasting and competing effects between elevation gradient and extent of tree cover loss control the magnitude of warming; locally ~> 70% of the tree cover loss is sufficient to fully offset the cooling effect of elevation and generate as much Tmax warming as in the lower montane forest.

Deforestation has stronger impact on CBH increase than climate change
The impact of deforestation on CBH was compared to that of climate change (Fig. 4). Deforestation (2003–2022) had a greater impact on the magnitude of CBH increase than climate change (1992–2022). While deforestation increased CBH across montane forests in Africa, climate change, based on ERA5-Land data from 1992 to 2022, exhibited an increase in 62% and a decrease in 37% of the study area. The net effect was an increase in most (76%) areas of montane forest. In areas where deforestation- and climate change-induced CBH changes are occurring in phase, the CBH increase was magnified. Similar patterns were obtained for Tmax change (i.e., deforestation increased Tmax more than climate change) (see Supplementary Fig. 3).

The impacts of deforestation and climate change on CBH at various elevation levels were also evaluated (Fig. 5). The result showed that while elevation reduced the impact of deforestation on CBH (R2 = 0.87, deviance explained = 88%, P < 0.001), its effect on the climate change-induced CBH shift was relatively smaller (R2 = 0.52, deviance explained = 53%, P < 0.001). At a higher elevation (> ~2250 m), deforestation- and climate change-induced CBHs showed an opposite pattern (i.e., with the former decreasing and the latter increasing with elevation).

Methods
Montane forest extent in Africa
The extent of montane forest was defined based on elevation, tree cover, and local elevation range (LER)60. The USGS Digital Elevation model (DEM) with a spatial resolution of 30 m was used to calculate elevation and LER. LER was calculated as the difference between the maximum and minimum elevation within a 5-km radius for each pixel. All pixels with an elevation of 1200–3500 m a.s.l., based on regional definition of montane forest in Africa4,61, > 30% tree cover, and LER > 300 m were considered to be montane forest. The tree cover threshold used was based on the definition of forest (i.e., tree cover 10–30%). Tree cover for year 2000 was processed to discard tree losses that occurred before 2003 to match our analysis period of 2003–2022. The montane forest definition used in this paper does not include subtropical and Mediterranean montane forests, or islands in Africa.

Identifying deforestation locations between 2003 and 2022
To identify areas affected by tree loss, we used a combined approach based on the GFC product from Landsat time series62 and a normalized difference fractional index (NDFI) derived from the spectral mixture model applied to Landsat63, which has been successfully used in previous studies to monitor deforestation in the tropics64,65. We used surface reflectance products from Collection 2 Landsat (5, 7, and 8) at 30-m resolution to compute the NDFI. To identify stable forest in 2003, all pixels with tree loss recorded between 2001 and 2003 were excluded using the GFC product. If a pixel experienced tree loss after 2003 (i.e., from 2004 to 2022), these pixels were retained. As a pixel can recover from deforestation between 2003 and 2022, a further filtering step was performed using the NDFI trend during this period. This step was necessary because the GFC product only records the presence or absence of tree cover gains between 2000 and 2012. Pixels that showed tree cover gain were discarded through this process. Finally, tree loss pixels identified by the GFC product and confirmed by the NDFI trend (i.e., showing statistically significant tree loss between 2003 and 2022) were used for analysis in this paper.

Maximum and minimum air temperature modelling
Air and dew point temperature predictions were made using an ensemble machine learning approach (Supplementary Fig. 6). A Tmax random forest regression model was built using five predictors (land surface temperature, normalized difference vegetation index, albedo, latitude, and longitude) separately for MODIS Terra and Aqua sensors (Eqs. 1 and 2), after performing a spatial forward feature selection using the ffs function in the “CAST” R package. Daytime land surface temperature (LST) data from version 6.1 MOD11A1 Terra (10:30 am) and MYD11A1 Aqua (1:30 pm) at 1-km resolution and daily temporal frequency were used66,67, as radiometric thermal emission from the surface directly affects the air temperature. Only good-quality clear sky pixels with LST error ≤ 1 K were filtered and used in our analysis by applying the flag bits of the quality control layer.

To represent vegetation cover and the fraction of solar radiation reflected or absorbed by a surface, the NDVI calculated from the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) product (MCD43A4 version 6) and the shortwave broadband albedo product (MCD43A3 version 6.1) at 500-m resolution were used. Latitude and longitude were also used, as air temperature depends on geographic location. All data were spatially and temporally harmonized to 1-km resolution and monthly temporal resolution. The Tmax model from Terra and Aqua was used as input for the ensemble learning (Eq. (3)). Random forest regression was used both as a base learner in Eqs. (1), (2), (4), and (5) and as a meta-learner in Eqs. (3) and (6) using “randomForest” version 4.7-1.1 R package. Tmin model was predicted following a similar approach but using night-time LSTs data from Terra (10:30 pm) and Aqua (1:30 am) (Eqs. (4)–(7)). The average air temperature Tmean was calculated from Tmax and Tmin using Eq. (7).

Tmax(Terra) = f[LSTday(10:30am), NDVI, albedo, latitude, longitude] (1)

Tmax(Aqua) = f[LSTday(1:30pm), NDVI, albedo, latitude, longitude] (2)

Tmax = f[Tmax(Terra), Tmax(Aqua)] (3)

Tmin(Terra) = f[LSTnight(10:30pm), NDVI, albedo, latitude, longitude] (4)

Tmin(Aqua) = f[LSTnight(1:30am), NDVI, albedo, latitude, longitude] (5)

Tmin = f[Tmin(Terra), Tmin(Aqua)] (6)

Tmean = (Tmax + Tmin) / 2 (7)

Dew point temperature modelling
The dew point temperature (Tdew) was modelled in three steps. First, saturated vapour pressure (es) was estimated using Tmin (Terra) and Tmin (Aqua) separately, as Tmin is considered to be closer to Tdew68 (Eqs. (8) and (9)). Second, Tdew was modelled for Terra and Aqua using random forest regression (Eqs. (10) and (11)). Third, Tdew (Terra) and Tdew (Aqua) were ensembled using random forest to estimate Tdew (Eq. (12)).

es(Terra) = 0.6108 exp[(17.27 × Tmin(Terra)) / (Tmin(Terra) + 237.3)] (8)

es(Aqua) = 0.6108 exp[(17.27 × Tmin(Aqua)) / (Tmin(Aqua) + 237.3)] (9)

Tdew(Terra) = f[es(Terra), LSTnight(10:30pm), latitude, longitude] (10)

Tdew(Aqua) = f[es(Aqua), LSTnight(1:30am), latitude, longitude] (11)

Tdew = f[Tdew(Terra), Tdew(Aqua)] (12)

Model validation and spatial autocorrelation test
For model training (70%) and testing (30%), 2-m air and dew point temperature monthly station data for 2003 and 2022 were used (see Supplementary Fig. 7 for the number of samples used for each month, which ranges from 332 to 498 samples) from the National Climate Data Center Global Summary of the Day (GSOD) dataset, National Centers for Environmental Information, National Oceanic and Atmospheric Administration (NOAA), USA69 (see Supplementary Fig. 7 for GSOD station locations). The GSOD data are available as daily averages, aggregated from hourly station data. We used daily maximum and minimum values. All daily observations were aggregated to a monthly timescale prior to analysis. The model performances for Tmax, Tmin, and Tdew are presented for each month using root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) (see Supplementary Figs. 8–10 and Tables 1–3). The presence of spatial autocorrelation in the model was evaluated using spatial cross-validation70 (Supplementary Fig. 11a–c).

For independent external model validation, air and dew point temperature data from local automatic weather stations in montane forests in Ethiopia (Bale Mountain, Supplementary Fig. 12), Kenya (Taita Hills, Supplementary Fig. 13), and Tanzania (Kilimanjaro Mountain, Supplementary Fig. 14) were used. Data from Ethiopia and Tanzania were obtained from the University of Marburg and data from Kenya from the University of Helsinki. The weather station data, which were collected at 30-minute intervals, were aggregated to a monthly time step to match the temporal scale of our analysis. The validation is presented in Supplementary Fig. 15a–i.

Estimating deforestation-induced temperature change
The deforestation-induced temperature change (surface, air, or dew point) between 2003 and 2022 was calculated for each pixel using Eqs. (13) and (14). The total temperature change (ΔT) between two years consists of a combined signal from deforestation (ΔTfcc) and climate change (ΔTcc). Hence, the deforestation-induced temperature change (ΔTfcc) was computed by subtracting the climate change signal from the total temperature change using a similar approach in previous studies16,20.

ΔT = ΔTfcc + ΔTcc (13)

ΔTfcc = ΔT − ΔTcc (14)

ΔTcc was calculated from the temperature change (surface, air, dew point) in nearby stable forests located within 5 km of the tree loss pixels. Stable pixels with no statistically significant change in tree cover and within the elevation range of the deforestation pixels were considered. This was done by first identifying the maximum and minimum elevation of the deforestation pixels and selecting stable pixels within the same elevation range. Furthermore, inverse distance weighting (IDW) was applied to minimize the influence of distance on the ΔTcc calculation from stable reference forest pixels (Eq. (15))16.

ΔTcc = [Σ (ΔTx / dx)] / [Σ (1 / dx)] (15)

where ΔTx (°C) is the temperature change between stable reference forest and tree cover loss pixels (x).

Estimating deforestation-induced CBH change from 2003 to 2022
The cloud base height (in metres), which is the height at which the atmosphere becomes moisture saturated under adiabatic conditions, was estimated by applying the Epsy equation28 (Eqs. (16) and (17)). The equation is accurate to within 2% for relative humidity above 50% and temperature between 0 °C and 30 °C28. The CBH change (ΔCBH) included a combined signal from forest loss (ΔCBHfcc) and the background climate change signal (ΔCBHcc). ΔCBHfcc was calculated by removing ΔCBHcc from our analysis using stable nearby reference forest pixels, following an approach that described the temperature change (Eqs. (18) and (19)). Validation of the CBH estimate against independent in situ CBH measurements in South Africa using a ceilometer is provided in Supplementary Fig. 16 (see also site description).

CBH(2003) = 125 × [Tair(2003) − Tdew(2003)] (16)

CBH(2022) = 125 × [Tair(2022) − Tdew(2022)] (17)

ΔCBH = ΔCBHfcc + ΔCBHcc (18)

ΔCBHfcc = ΔCBH − ΔCBHcc (19)

where Tair and Tdew are deforestation-induced air temperature and dew point temperature in 2003 and 2022, respectively. ΔCBHfcc was used to identify whether the rising temperature due to forest loss in montane forests has lifted the CBH, affecting the ability of montane forests to intercept cloud water, or not.

Climate change-induced CBH change from 1992 to 2022
The climate change-induced changes in CBH that are unrelated to deforestation operate on a larger spatial scale than deforestation and are estimated using Eqs. (16) and (17) but using 30 years (1992 to 2022) of air temperature and dew point temperature data from ERA5-Land71. In ERA5-Land, vegetation (e.g., leaf area index) does not show inter-annual variation72. This static vegetation state makes it preferable for estimating CBH change due to climate change, as it is not affected by tree cover change signals. Selecting ERA5-land over other reanalysis data is due to its relatively high spatial resolution (~10 km) and better accuracy when evaluated against in situ station data in montane forests of Africa (See Supplementary Fig. 17).

We used R 4.3.0 for data analysis and QGIS version 3.30.2 for map composition.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an exceptional submission and deserves to be published as soon as possible. The findings are highly important and need to be made available in the general literature.\nI did think that a number of references were perhaps missing, which might help extend some of the analysis in the paper. These, I think, are worth considering in more detail. However, I do not wish to \"impose\" any of these references on the Authors. They should be considered as potential additional references and possibly discussion points in the framework of a minor revision.\nhttp://www.sciencedirect.com/science/article/pii/S0921818110001773\nPepin et al make the argument that the loss of tree cover leads to less ET being deposed on glaciers in the region (Kenya)... See perhaps also Moelg et al\n(https://www.degruyter.com/document/doi/10.1525/9780520934245-013/html)\nThe following paper by Gerbrehiwot et al\n(https://onlinelibrary.wiley.com/doi/abs/10.1002/wat2.1317) makes the argument that the waters of the Blue Nile Basin depend at least in part on ET from places like the Congo Basin and the West African rainforest. Your submission here really talks about the \"receiving end\" of this relationship (though certainly not less important). Furthermore, this general relationship is also important with regard to downstream relationships (outcomes) in the Blue Nile Basin and beyond. Some of these points may be worth mentioning here...\nIt is probably also useful to cite more of the literature on cloud forests and downstream water availability. Thus, for example, you might highlight:\nhttps://global.oup.com/academic/product/tropical-rain-forest-ecology-diversity-and-conservation-9780199285884\nhttp://dx.doi.org/10.1002/hyp.7974\nFinally, I noticed that your T analysis also estimates the impact of albedo... I do have questions about whether or not this is the best possible measure of impacts on T. In this regard, you might consider the findings of the following paper:\nhttps://onlinelibrary.wiley.com/doi/10.1111/gcb.17195\nThe following two references may also be useful:\nhttp://onlinelibrary.wiley.com/doi/10.1002/joc.3409/abstract\nhttps://doi.org/10.1002/joc.3566\nI would like to emphasize that these suggestions are provided in the hopes of helping to broaden and strengthen the discussion in this submission. I think this is generally both an excellent and important paper and look forward to seeing it eventually in published form.", "reviewer_2": "In summary, this study makes an important advance in quantifying the climatic impacts of montane deforestation in Africa with novel findings that deepen our understanding of how these effects are mediated by elevation and compare to climate change. The results have significant implications for the conservation of these ecosystems and the critical water provisioning services they provide. I recommend publication with some technical clarifications as noted.\n-While previous studies have examined montane deforestation impacts in specific locations (mainly in the Neotropics) and often focus on surface temperature, this work is novel and valuable in its Africa-wide scope, use of air temperature which is more ecologically relevant, and consideration of CBH which has important consequences for moisture inputs and cloud immersion. The literature review demonstrates the research addresses an important gap. I am curious to understand the comparison between montane forest deforestation and effects and the northern Andes montane forest, as well as moisture recycling?\n-The results that 1) Montane deforestation significantly increased air temperature and CBH in Africa from 2003-2022, with the magnitude mediated by elevation and extent of tree loss, and 2) These climatic impacts of forest loss were greater than those from background climate change over this period or over the 1992-2022 period? I do not understand why they are different in time span.\n-The methodology of combining extensive remote sensing data, machine learning models validated with weather stations, and robustness checks gives confidence to the findings, suggestions:\n- State the sample sizes and rational for 70/30 training/test split for the RF models\n- Discuss implications of 1-km scale for capturing finer-scale microclimatic gradients in montane forests\n- Quantify variable importance and hyperparameter tuning for RF in supplement\n- Elaborate how locations and elevations were matched when extracting reference pixels for background climate signal\n- List years of station data used for validation to clarify temporal coverage\nMore details that support reproducibility could be provided in the supplement, but the core methods are sufficiently described for the main text.", "reviewer_3": "#Review comments\nThis manuscript delves into an important and timely topic of the understudied role of tropical montane forests have on climate change especially how their loss affects biodiversity, warming and cloud level rise in Africa. It reveals how deforestation is a major cause of biodiversity loss, impacts on Cloud base height (CBH) and warming which is definately contributing the limited knowledge on the African tropical montane forests. The authors further comprehensively assessed and demonstrated how climate change can impact on CBH and warming. The study provides the essential observational evidence that shows response of air temperature and CBH to deforestation in African montane forests over a period. The study also demonstrates the magnitude of forest loss due to deforestation between 2003 and 2022 and shows that it has led to notable dynamics in maximum air temperature and cloud base height by respective values with various interplay of other factors. The study further indicates that the warming effect of deforestation diminishes with elevation, with climate change effects having the opposite effect. However, tree cover reductions exceeding 70% were enough to counteract the cooling effect of elevation, causing comparable increases in warming and cloud base height even at higher elevations (> 1600 m above sea level).\nThe methodology adopted by the authors are sound and the remotely sensed data they have assembled is very extensive and sufficient and can be easily reproduced.\nI enjoyed reading the manuscript. It is generally well written and structured with minimum errors. The figures are clear and neat with the descriptions and are much self-explanatory. And I feel the Manuscript will be of great value and interest to the readers of this journal.\nHowever, the Manuscript needs to be revised before it can be published.\n#Major Revision\nThe authors need to improve on some of the main (section) text i.e. by showing the necessary statistics on the recent trends of deforestation, especially for the tropical montane forest in Africa and as well at the global scale: there is numerous literature that can offer the authors this information.\nThe manuscript talks about deforestation affecting biodiversity loss in the tropical montane forests, but do not have any linkages how this was assessed. I would suggest the authors add a section on how biodiversity is affected by deforestation, climate change especially after the findings showing that indeed deforestation and Climate change have an influence on CBH and warming.\nImprove on the discussion section by linking their results with the literature and demonstrate how their study has advanced knowledge which is lacking in the tropical montane forest especially in Africa.\nAre there similar studies in the rest of the tropical montane forests in the other continents? Or montane forests out of the tropics? How do they compare with the African tropical montane forests in this current study? This would give a very good context to understand where the tropical montane forests in Africa are.\n#Minor Revision\nComments directly placed on the Manuscript PDF file."}, "author_response": "Response to reviewers’ comment\nReviewer #1 (Remarks to the Author):\nThis is an exceptional submission and deserves to be published as soon as possible. The findings are highly important and need to be made available in the general literature.\nResponse #1\nWe thank the reviewer for the very positive recommendations on our manuscript.\nI did think that a number of references were perhaps missing, which might help extend some of the analysis in the paper. These, I think, are worth considering in more detail. However, I do not wish to \"impose\" any of these references on the Authors. They should be considered as potential additional references and possibly discussion points in the framework of a minor revision.\nResponse #2\nWe included all suggested references (e.g., Pepin et al., 2010; Gerbrehiwot et al., 2018; Bruijnzeel et al., 2010; Ellison et al., 2024) among others and added new paragraphs in the Discussion sections of the manuscript to put our results in the context of literatures (e.g., lines 79-84, 201-205, 218-238, and 258-269).\nhttp://www.sciencedirect.com/science/article/pii/S0921818110001773\nPepin et al make the argument that the loss of tree cover leads to less ET being deposed on glaciers in the region (Kenya)... See perhaps also Moelg et al\n(https://www.degruyter.com/document/doi/10.1525/9780520934245-013/html)\nThe following paper by Gerbrehiwot et al\n(https://onlinelibrary.wiley.com/doi/abs/10.1002/wat2.1317) makes the argument that the waters of the Blue Nile Basin depend at least in part on ET from places like the Congo Basin and the West African rainforest. Your submission here really talks about the \"receiving end\" of this relationship (though certainly not less important). Furthermore, this general relationship is also important with regard to downstream relationships (outcomes) in the Blue Nile Basin and beyond. Some of these points may be worth mentioning here...\nIt is probably also useful to cite more of the literature on cloud forests and downstream water availability. Thus, for example, you might highlight:\nhttps://global.oup.com/academic/product/tropical-rain-forest-ecology-diversity-and-conservation-9780199285884\nhttp://dx.doi.org/10.1002/hyp.7974\nFinally, I noticed that your T analysis also estimates the impact of albedo... I do have questions about whether or not this is the best possible measure of impacts on T. In this regard, you might consider the findings of the following paper:\nhttps://onlinelibrary.wiley.com/doi/10.1111/gcb.17195\nhttp://onlinelibrary.wiley.com/doi/10.1002/joc.3409/abstract\nhttps://doi.org/10.1002/joc.3566\nResponse #3\nWe agree with the comment that the albedo impact on T can be smaller specially in low-latitudes (tropics) than high-latitudes. Here we used albedo as it helps to approximate the change in the amount of incoming solar radiation reflected or absorbed during deforestation, which can slightly impact T through its radiative forcing. Our T model (new section in Supplementary Material max “Miscellaneous information related to temperature model”, page 8) further supports that albedo has smaller impact on T as it is the least important variable in predicting T.\nI would like to emphasize that these suggestions are provided in the hopes of helping to broaden and strengthen the discussion in this submission. I think this is generally both an excellent and important paper and look forward to seeing it eventually in published form.\nResponse #4\nThank you.\nReviewer #2 (Remarks to the Author):\nIn summary, this study makes an important advance in quantifying the climatic impacts of montane deforestation in Africa with novel findings that deepen our understanding of how these effects are mediated by elevation and compare to climate change. The results have significant implications for the conservation of these ecosystems and the critical water provisioning services they provide. I recommend publication with some technical clarifications as noted.\nResponse #1\nWe thank the reviewer for the very positive recommendations on our manuscript.\nWhile previous studies have examined montane deforestation impacts in specific locations (mainly in the Neotropics) and often focus on surface temperature, this work is novel and valuable in its Africa-wide scope, use of air temperature which is more ecologically relevant, and consideration of CBH which has important consequences for moisture inputs and cloud immersion. The literature review demonstrates the research addresses an important gap. I am curious to understand the comparison between montane forest deforestation and effects and the northern Andes montane forest, as well as moisture recycling?\nResponse #2\nRegarding effects of montane deforestation in the northern Andes, we speculate that similar effects to the montane deforestation in Africa might happen (i.e., air temperature and CBH can increase in response to deforestation and less moisture can recycling due to reduced evapotranspiration). The montane deforestation in Andes or elsewhere in tropics can affect water availability, not only in the 2 source area of deforestation, but in remote areas elsewhere through teleconnection of moisture transport and recycling (Espinoza et al., 2020; Pepin et al., 2010). But this speculation requires further study as there can be two-way feedback in the Amazon-Andes hydroclimate interconnection (Espinoza et al., 2020), as moisture inputs from the Amazon to the Andes Mountain might counteract, stabilize, or reduce the deforestation impact in the Andes.\nTo address the comment, we have included additional sentences in the discussion section (line 245-247) as “Deforestation also affects the availability of fresh water supply locally through reduction in evapotranspiration at the source area of tree cover loss and remotely via reducing atmospheric moisture recycling and transport (34,35,36, 37).”\nReferences\nEspinoza et al., 2020. Hydroclimate of the Andes Part I: Main Climatic Features, Front. Earth Sci.\nhttps://doi.org/10.3389/feart.2020.00064\nPepin et al., 2010. The montane circulation on Kilimanjaro, Tanzania and its relevance for the summit ice fields: Comparison of surface mountain climate with equivalent reanalysis parameters. Glob. Planet. Change https://doi.org/10.1016/j.gloplacha.2010.08.001\n-The results that 1) Montane deforestation significantly increased air temperature and CBH in Africa from 2003-2022, with the magnitude mediated by elevation and extent of tree loss, and 2) These climatic impacts of forest loss were greater than those from background climate change over this period or over the 1992-2022 period? I do not understand why they are different in time span.\nResponse #3\nThank you for the comment. The climate change period refers to 30 years (1992-2022). The reason for using a different time span for the climate change is related to fulfilling the requirements of its definition, which refers to the long-term change in climate over a period of minimum 30 years. Nonetheless, when the same time span was used, the impact of deforestation is stronger in magnitude than what was reported in the manuscript.\nTo clarify this, we have added time reference in the sentences (line 170 and 171) as “The impact of deforestation on CBH was compared to that of climate change (Figure 4). Deforestation (2003 – 2022) had a greater impact on the magnitude of CBH increase than climate change (1992 –2022).\n-The methodology of combining extensive remote sensing data, machine learning models validated with weather stations, and robustness checks gives confidence to the findings, suggestions:\n- State the sample sizes and rational for 70/30 training/test split for the RF models\nResponse #4\nWe now state the sample sizes in the main manuscript (lines 393-394). Details of the sample for each month are also provided in Supplementary Figure 6. The modified text now reads “For model training (70%) and testing (30%), 2-m air and dew point temperature monthly station data for 2003 and 2022 were used (see Supplementary Fig. 6 for the number of samples used for each month, which ranges from 332 to 498 samples).\n- Discuss implications of 1-km scale for capturing finer-scale microclimatic gradients in montane forests\nResponse #5\n- Quantify variable importance and hyperparameter tuning for RF in supplement\nResponse #6\nWe now included these in the supplementary material (see new section in Supplementary Material “Miscellaneous information related to temperature model”, page 8).\n- Elaborate how locations and elevations were matched when extracting reference pixels for background climate signal\nResponse #7\nWe added text for clarification (lines 425-427), which reads “Stable pixels with no statistically significant change in tree cover and within the elevation range of the deforestation pixels were considered. This was done by first identifying the maximum and minimum elevation within the forest loss pixel and excluding those reference stable pixels outside this elevation range”.\n- List years of station data used for validation to clarify temporal coverage\nMore details that support reproducibility could be provided in the supplement, but the core methods are sufficiently described for the main text.\nResponse #8\nFor validation (as well as training) we used GSOD monthly data for the year 2003 and 2022 as stated in Supplementary Figure 6. We have now clarified this in the main manuscript as well, which reads “For model training (70%) and testing (30%), 2-m air and dew point temperature monthly station data for 2003 and 2022 were used (see Supplementary Fig. 6 for the number of samples used for each month, which ranges from 332 to 498 samples)” (line 395-396).\nIn addition, more details are now included both in the supplementary material (see new section in Supplementary Material “Miscellaneous information related to temperature model”, page 8) and Methods (line 353-354, 367, and 467) for reproducibility of our approach.\nReviewer #3 (Remarks to the Author):\n#Review comments\nThis manuscript delves into an important and timely topic of the understudied role of tropical montane forests have on climate change especially how their loss affects biodiversity, warming and cloud level rise in Africa. It reveals how deforestation is a major cause of biodiversity loss, impacts on Cloud base height (CBH) and warming which is definately contributing the limited knowledge on the African tropical montane forests. The authors further comprehensively assessed and demonstrated how climate change can impact on CBH and warming. The study provides the essential observational evidence that shows response of air temperature and CBH to deforestation in African montane forests over a period. The study also demonstrates the magnitude of forest loss due to deforestation between 2003 and 2022 and shows that it has led to notable dynamics in maximum air temperature and cloud base height by respective values with various interplay of other factors. The study further indicates that the warming effect of deforestation diminishes with elevation, with climate change effects having the opposite effect. However, tree cover reductions exceeding 70% were enough to counteract the cooling effect of elevation, causing comparable increases in warming and cloud base height even at higher elevations (> 1600 m above sea level).\nThe methodology adopted by the authors are sound and the remotely sensed data they have assembled is very extensive and sufficient and can be easily reproduced.\nI enjoyed reading the manuscript. It is generally well written and structured with minimum errors. The figures are clear and neat with the descriptions and are much self-explanatory. And I feel the Manuscript will be of great value and interest to the readers of this journal.\nResponse #1\nWe thank the reviewer for the very positive recommendations on our manuscript.\nHowever, the Manuscript needs to be revised before it can be published.\nResponse #2\nWe have considered the comments and revised the manuscript as detailed below.\n#Major Revision\nThe authors need to improve on some of the main (section) text i.e. by showing the necessary statistics on the recent trends of deforestation, especially for the tropical montane forest in Africa and as well at the global scale: there is numerous literature that can offer the authors this information.\nResponse #3\nWe have accepted the comment and provided background information (including trends of deforestation globally, whole Africa, locally, and its major drivers) from the literature in the Introduction text (line 77-82) as “In particular, while montane deforestation is accelerating globally (at an annual rate of 0.31%), tropical montane forests in Africa have experienced the highest rate of deforestation in the last two decades (0.48% per year) (24). Although controversial and geographically variable, local rates of montane deforestation in unprotected areas of Africa can be as high as 3% per year (25). The main driver of montane deforestation in Africa has been attributed to small-scale cropland expansion, with other factors (e.g., urbanisation, large-scale commodity crops and forest fires) playing a lesser role (26,27).”\nThe manuscript talks about deforestation affecting biodiversity loss in the tropical montane forests, but do not have any linkages how this was assessed. I would suggest the authors add a section on how biodiversity is affected by deforestation, climate change especially after the findings showing that indeed deforestation and Climate change have an influence on CBH and warming.\nResponse #4\nWe included a new paragraph (line 258-269), which reads “Many tropical montane species live in a narrow elevational and climatic range where the montane forests serve as refugia for the survival of plant and animal species (1,2,3,4). However, deforestation-induced warming and CBH-increase can affect biodiversity of montane forest in many aspects (41). For example, an increase in air temperature alters the microclimate, which is essential for bio-diversity conservation through its influence on vascular plant species richness, turnover, and composition (43,44,45). As the biodiversity of plant and animal species is regulated by thermal tolerance, an increase in air temperature can directly affect the distribution and diversity of organisms and increase their risk of extinction by altering the unique hydro-climatic environment that serves as a micro-refuge for different species (44,45). Raising CBH also affects biodiversity through its effects on humidity, light conditions, and water availability, which are critical for the survival of organisms (46,47). For example, an increase in CBH leads to a decrease in humidity, cloud cover, and availability of water from cloud water interception, which are critical for the population size and survival of species (48,49).\nImprove on the discussion section by linking their results with the literature and demonstrate how their study has advanced knowledge which is lacking in the tropical montane forest especially in Africa. Are there similar studies in the rest of the tropical montane forests in the other continents? Or montane forests out of the tropics? How do they compare with the African tropical montane forests in this current study? This would give a very good context to understand where the tropical montane forests in Africa are.\nResponse #5\nWe have accepted the comment and added sentences as well as new paragraphs in the discussion section (lines 199-205, 218-238, and 258-269). Copies of the new paragraphs are copied here below.\nLines 218-238\nCompared to previous studies on deforestation-induced cloud regime change in tropical montane forests, our findings provide additional insights. For instance, a previous study showed that lowland deforestation increases the cloud base height in nearby montane forest in Monteverde in Costa Rica (11). Also, in situ study in the subtropics indicated that the CBH can lift in response to drought and hurricane-induced canopy defoliation in the Luquillo submontane forest in eastern Puerto Rico (8). Our study extends this understanding to the highlands of Africa, showing that human-induced montane deforestation can increase CBH at the source area of deforestation in Africa. This means that local deforestation in the montane forests deserves attention as it can directly affect the cloud regime lying above the location of deforestation through warming and reduced moisture recycling, as well as in remote areas through teleconnection of moisture transport (7,34,35,36, 37). This additional information can help to raise awareness of the need to mitigate the impacts of montane deforestation and to conserve and restore of montane forests both inside and outside protected areas.\nOur results on climate-induced changes in CBH in African montane forests showed a slightly different pattern than previous global studies (38, 39) over the last two decades. For example, both climate models and satellite-based studies reported an average increase in cloud height in response to climate change over the tropics (38,39). In contrast to these studies, our CBH change showed a latitudinal pattern (i.e., areas south of ~ 17°S latitude showed a decrease in CBH, while those montane forests located north of ~ 17°S latitude, where most of the montane forests in Africa occur, showed an increase in CBH on average). For those areas that showed a decrease in CBH (i.e., south of ~ 17°S latitude), whether the decrease in CBH is related to their geographic proximity to the ocean, differential impacts of climate change, size of montane forest, or other reasons requires further investigation.\nLines 257-267 (see Response #4 for this paragraph)\n#Minor Revision\nComments directly placed on the Manuscript PDF file.\nResponse #7\nWe thank the reviewer for the comments. Below, we have provided a point-by-point reply to the comments copied from the annotated PDF in their order of appearance.\nThe title in the current state limits the significant work the authors have undertaken, it makes it appear as though the authors are ONLY assessing how montane forest loss contributes to climate change effects on warming & CBH yet in reality they have assessed how the loss of tropical montane forests due to deforestation affects CBH & warming. They have also collected extensive data and assessed how climate change affects CBH and warming.\nI suggest they authors revise the title to cover the significant work they have undertaken.\nResponse #8\nWe have accepted the comment and slightly modified the title to better reflect the content of the study as “Deforestation amplifies climate change effects on warming and cloud level rise in African montane forest”.\nReviewer comment on the Main section\nCan you specify this nearby forest? say as: ..regime of XXX montane forest in XXX (ref. 11, 14)\nResponse #9\nDone (line 56).\nWhat does the literature say in terms of impacts of deforestation on local temperature. Can you give examples of the figures showing these impacts?\nResponse #10\nDone (line 69-70).\nRevise this sentence for clarity so that the focus of impacts of montane deforestation on CBH & warming?\nResponse #11\nDone (line 74-75).\nCould you state how high deforestation rates have been is it increasing or decreasing in the past 2 decades?\nResponse #12\nGFC has been reported to have it's limitations such as underestimation and over estimation of forest cover change at local scales. What have the author done to remedy these inconsistencies?\nResponse #13\nOne source of error in the GFC product, for forest cover change analysis, is the unavailability of forest gain information after 2012. This means, for example a forest loss pixel identified in 2013 could recover in 2022 and in the absence of forest gain information, the pixel could be considered as forest loss. This increases false detecting error or can cause over estimation of forest loss. To reduce this problem, we used a forest cover change detection algorithm (spectral mixture model) based on normalized difference fractional index (NDFI) as we have described in the methodology section. We used NDFI trend to check whether the forest loss pixels identified by GFC product recovered after deforestation or not. If a pixel recovered its forest cover, we filtered and discarded those pixels. This approach can improve the inconsistency as also indicated in our accuracy assessment (based on reference high-resolution Google imagery and Planet monthly mosaics, Supplementary Fig. 17) that showed smaller false detection error of 7.5% compared with the omission error (18.7%) (refer Uncertainties and limitation section for details; lines 296-302). Furthermore, the relatively good overall accuracy (95.5% ± 1.1%) of our forest loss detection result indicates sufficient level of confidence in the approach. However, we believe that still further study needs to be done to reduce the omission error.\nReviewer comment on the result section\nFigure 3. Very clear and excellent finding. Could there be an explanation why beyond the various thresholds listed, the trajectory shows a steep increase regardless of elevation?\nResponse #14\nWe have clarified the sentence (line 155-156) as “….this trajectory is reversed and the warming and CBH increase steeply regardless of elevation as these thresholds were enough to start offsetting the elevation effect (Figure 3a–c).\nFigure 4. Could the red, blue and black lines be made thinner?\nResponse #15\nDone.\nCould the patterns of deforestation influence climate change - induced CBHs? Can the authors add more explanation on Deforestation/Climate change relationship? The point is, deforestation itself affects climate change and there should be an expectation that where the deforestation rate is high, then it affects climate change.\nResponse #16\nThank you for the comment. We agree that deforestation itself can affect climate change-induced CBH through its impact on air temperature via biogeophysical and biogeochemical mechanisms. Nonetheless, our study compared the two separately and does not consider these feedbacks as this requires a different study approach (e.g., a coupled land-atmosphere modelling). To address the comment, we have include additional explanation in the discussion (Uncertainties and limitation section, lines 310-312), which reads “On the other hand, deforestation itself can amplify the impact of climate change on CBH through increasing the air temperature via biogeophysical and biogeochemical mechanisms (15,16,51) but these feedbacks were not included in our study. “\nA very clear results! The montane forests have high deforestation happening at the lower elevation as compared to the higher elevation.\nReviewer comment on the discussion section\nThe authors could expound on this discussion by showing or referring to the specific findings e.g; Their findings Vs what the previous studies have reported\nResponse #18\nWe have added new paragraphs in the discussion section (see Response #4 and #5 for details).\nThe authors talk about the deforestation being a cause of biodiversity loss yet they focus on its impact on CBH and warming only. It would be worthy to add an explainer linkage between CBHs and Warming on biodiversity.\nResponse #19\nWe included these in the discussion section (see Response #4 for details).\nCan the authors link these discussions to some of the specific results, such as where there was a comparison between the impacts of deforestation on CBH and as well of climate change on CBH.?\nResponse #20\nWe added new paragraph in the discussion section (see Response #5 for details).\nCan you remove this ''in response'' or revise the sentence?\nResponse #21\nDone.\nAs asked earlier, what have authors done to remedy these uncertainities? especially at local scales?\nResponse #22\nPlease refer to Response #13 for details.\nReviewer comment on Method section\nHow do you harmonise the differences in the definition of forest say GFC Vs UNFCC?\nResponse #23\nTo make the two definitions more compatible, we did the following. The GFC forest definition uses trees height threshold of 5m and tree cover is provided from 0 to 100% at 30m spatial resolution (~ 900 m² or 0.09 ha). UNFCC is flexible in its forest definition in terms of tree height (minimum 2-5m), tree cover (minimum 10-30%), and minimum area of land (0.05-1.0 hectares). Hence, to harmonize the GFC to UNFCC forest definition, we used the highest minimum tree cover threshold of ≥ 30% and discarded the rest pixels. The reason behind using 30% tree cover for UNFCC is twofold. The first reason is related to consistency. Meaning, if we use 10% tree cover, which is the lowest minimum threshold, the corresponding height is 2m, which is not available in GFC product. Using the highest minimum threshold for both height and tree cover improves the consistent of the forest definition. The second reason is accuracy issues (i.e., the use of a higher threshold reduces the risk of including some croplands occurring in the agroforestry in the highlands that can meet the lowest threshold (10% tree cover and 2m height) (e.g., false banana trees in agricultural land in montane forest areas in Taita Hills, Kenya) (see Methods, lines 327-331)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have thoroughly addressed all my previous comments and made the necessary revisions. Therefore, I recommend the manuscript for publication.", "reviewer_3": "The authors have addressed the comments clearly and the manuscript can now be published in this current revised form."}, "author_response": ""}]

Decision letter
REVIEWERS' COMMENTS
Reviewer #2 (Remarks to the Author):
The authors have thoroughly addressed all my previous comments and made the necessary
revisions. Therefore, I recommend the manuscript for publication.
Reviewer #3 (Remarks to the Author):
The authors have addressed the comments clearly and the manuscript can now be published in
this current revised form.