Revealing the widespread potential of forests to increase low level cloud cover

Forests provide numerous ecosystem services, including carbon sequestration, which is crucial for climate change mitigation. Ambitious afforestation programs are underway globally to enhance carbon sinks and meet mitigation targets. However, the impact of altered forest cover on the climate system extends beyond carbon sequestration, encompassing biophysical effects. One such effect, often neglected, is the influence of afforestation on cloud regimes. Changes in cloud cover have significant repercussions on the hydrological cycle, surface radiation budget, and planetary albedo. This research addresses the gap in understanding the global-scale impact of afforestation on cloud formation. Planting trees affects climate through various mechanisms, including changes in albedo (reflectivity) and transpiration rates. Forests can modify the atmospheric boundary layer (ABL), influencing the formation of low-level convective clouds, potentially impacting precipitation and the Earth's radiative budget. The complexity of forest-atmosphere interactions, coupled with uncertainties in cloud behavior, necessitates a comprehensive global assessment to guide land-based climate mitigation strategies. Clouds play a critical role in regulating the Earth's radiative budget, with low clouds generally exerting a cooling effect through increased reflection of solar radiation. The formation of these clouds is influenced by several factors, including vegetation properties such as access to water, landscape heterogeneity, and the emission of biogenic volatile organic compounds (BVOCs). The interplay between these factors and their influence on cloud formation is complex, leading to substantial uncertainty in predicting the effects of land-use changes on cloud cover. Therefore, a global assessment that leverages satellite remote sensing data is vital for informing evidence-based afforestation and forest restoration policies.

Existing literature highlights the multifaceted role of forests in climate regulation. Studies have emphasized the importance of carbon sequestration in mitigating climate change, driving ambitious reforestation and afforestation initiatives. However, the biophysical impacts of forest cover change, particularly the influence on cloud formation, remain understudied. Some research suggests a link between forests and increased low-level cloud cover, attributing it to enhanced convection and moisture recycling. Other studies have focused on the direct impact of forests on surface albedo and energy balance, often showing a trade-off between carbon sequestration and surface temperature changes, particularly at higher latitudes. The existing literature presents a mixed picture, highlighting the need for a comprehensive, global-scale assessment of the biophysical effects of forests on cloud formation.

The study employs a space-for-time substitution approach using satellite remote sensing data to assess the effect of afforestation on low-level cloud cover. The methodology leverages two key datasets: cloud fractional cover (CFC) from the European Space Agency's Climate Change Initiative (CCI) and land fractional cover from the Land Cover CCI. The CFC data provides information on cloud cover at a 0.05° spatial resolution, while the land cover data identifies the fraction of pixels covered by various vegetation types. The analysis focuses primarily on transitions from low vegetation (grasses and crops) to forest (deciduous and evergreen). A linear regression model is employed within a moving window approach (7x7 pixels) to isolate the local effect of forest cover on cloud cover, while accounting for the compositional nature of land cover data through singular value decomposition (SVD). The method requires multiple assumptions: the primary sensitivity to low-level convective clouds, limited lateral advection of clouds between formation and satellite observation, and homogenous weather conditions within the moving window. Specific steps to account for topographic effects and ensure sufficient co-occurrence of vegetation types are incorporated. Post-processing includes decorrelation and weighted averaging to aggregate results to a coarser spatial resolution. To validate the space-for-time findings, the researchers employed an alternative method based on areas with actual forest cover changes over time. Additionally, ground-based cloud observations from SYNOP weather stations across Europe were utilized for further validation. The SYNOP data, paired based on proximity and forest cover differences, provides independent confirmation of the satellite-based findings. Finally, the study incorporates data on surface energy balance components (net radiation, latent heat flux, sensible and ground heat fluxes) to explore potential drivers of cloud formation.

The study's key findings demonstrate a widespread potential for afforestation to increase low-level cloud cover across the globe. Specifically: 1. **Global Pattern:** In 67% of the globally sampled areas, afforestation is projected to lead to an increase in low-level cloud cover. This percentage rises to over 74% during the warmer months (May-September). 2. **Seasonal Variation:** Marked seasonal patterns are observed in most regions, with a strong and consistent increase in cloud cover during the boreal summer, while some regions with prolonged snow cover show an inverse effect during winter and spring. 3. **Forest Type Dependence:** Different forest types exhibit varying effects on cloud formation. Needleleaf forests, especially in Europe, demonstrate a stronger cloud-generating effect compared to broadleaf forests. 4. **Methodological Robustness:** The findings are robust across multiple analytical approaches. The space-for-time results are validated using an alternative method based on actual land cover changes and independent ground-based cloud observations from SYNOP stations in Europe. The consistency across these approaches strengthens the confidence in the findings. 5. **Surface Energy Balance Link:** Analysis linking changes in cloud cover to surface energy balance components suggests a correlation between increased cloud cover and both increased latent heat flux and increased net radiation.

The study's findings directly address the research question by providing strong observational evidence of the widespread potential for afforestation to increase low-level cloud cover. This effect, often overlooked, is significant as it suggests an additional cooling mechanism associated with afforestation, counteracting the direct warming effect from reduced surface albedo. The results have important implications for climate change mitigation strategies, highlighting the need for a comprehensive approach that considers both carbon sequestration and biophysical effects. The observed dependence of cloud formation on forest type suggests that careful consideration of species selection is important for maximizing the climate benefits of afforestation programs. The validation through alternative methodologies and ground observations strengthens the reliability of the findings, offering robust evidence for policy-makers. The link between cloud formation and surface energy balance provides further insights into the underlying physical mechanisms involved.

This study offers a comprehensive, observationally-driven assessment of the biophysical effects of forests on cloud regimes. The main contribution is the demonstration of a widespread potential for afforestation to increase low-level cloud cover, leading to a cooling effect. This indirect effect should be integrated alongside carbon sequestration efforts to achieve optimal climate change mitigation. Future research should focus on quantifying the radiative forcing associated with these changes in cloud cover, exploring the impact on precipitation patterns, and further investigating the influence of specific forest types and environmental conditions.

The study's limitations primarily stem from the inherent complexities of the space-for-time methodology. The method focuses on local effects and may underestimate the magnitude of changes in cloud cover due to factors like cloud advection and non-local effects. The validation efforts mitigate this to some degree, but uncertainties remain. Additionally, the analysis relies on existing datasets, which have inherent uncertainties. Further research using coupled models is necessary to better understand non-local effects and fully quantify the radiative forcing associated with changes in cloud cover.