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

The study addresses how changes in forest cover, particularly afforestation and forest restoration, influence the climate system beyond carbon sequestration, focusing on indirect biophysical effects on low-level cloud formation. Forests alter energy, moisture, and momentum exchanges with the atmosphere, affecting boundary layer development, cloud formation, precipitation, and radiation budgets. Prior work highlighted trade-offs between carbon gains and albedo-driven warming, especially at high latitudes, and emphasized that land-based mitigation must incorporate both biogeochemical and biophysical processes. Low clouds generally cool climate, yet cloud responses remain a key uncertainty in climate models. Observational evidence suggests forests can enhance low-level convective clouds, but effects vary by region, season, vegetation type, and landscape structure. Given uncertainties and the policy relevance (e.g., European Green Deal afforestation plans), the authors undertake a global, observation-driven assessment to quantify where afforestation would increase low-level clouds and how this depends on season and forest type.

The paper synthesizes literature on forest-climate interactions, including: biogeochemical mitigation potential of forests and the limitations/trade-offs of large-scale tree planting; biophysical effects such as albedo changes, evapotranspiration, and roughness-driven boundary layer dynamics; observational and modeling studies showing forests and other vegetation (e.g., the US Corn Belt) can influence shallow convection, cloudiness, and precipitation; the role of cloud type in Earth's energy balance, with low clouds contributing net cooling; diurnal cycles of cumulus over land; the impact of snow cover and albedo contrasts on surface energy balance; influences of soil moisture, landscape heterogeneity/fragmentation, and biogenic volatile organic compounds (BVOCs) on cloud formation; and ongoing discrepancies between models and observations in biophysical responses to land cover change. The review motivates an observation-based, globally consistent analysis to benchmark models and inform land-based mitigation strategies.

Primary approach: A global space-for-time substitution method isolates the local effect of forest cover on low-level convective cloudiness by comparing neighboring pixels within moving windows, minimizing confounders. - Data: ESA Climate Change Initiative (CCI) Cloud Fractional Cover (CFrC) from MODIS-Aqua (2004–2014), monthly climatologies at 0.05°; ESA CCI Land Cover aggregated to 0.05° fractional cover for vegetation and other classes (deciduous forest, evergreen forest, herbaceous vegetation; plus shrublands, savannas, wetlands, water, bare/sparse, snow/ice, urban). - Assumptions: Sensitivity mainly to boundary-layer cumulus (low wind, early afternoon peak near 14:00 local), limited lateral advection at MODIS-Aqua overpass (~13:30), and locally homogeneous synoptic conditions over flat, inland terrain. - Estimation: Within a 7×7 pixel window (~35 km), apply a linear unmixing regression of monthly mean CFrC onto land cover fractional composition. To handle compositional predictors, perform SVD on centered X, regress in reduced space (Z), and predict CFrC for ‘pure’ cover types via transformed dummy compositions. Compute Δy as the difference between predicted pure forest and pure herbaceous states for two transitions: herbaceous→deciduous and herbaceous→evergreen. Estimate uncertainties from regression covariance. - Masking/filters: Require sufficient co-occurrence of target classes (co-occurrence index I ≥ 0.5) and flat orography (mean elevation <50 m, SD <100 m, local mean difference <100 m within window). Aggregate overlapping results decorrelating via overlap matrix and uncertainty-weighted averaging to ~0.35°; remove outliers (insufficient support, high uncertainty σa > 0.1, and outside 1st–99th percentiles). Merge deciduous and evergreen results weighted by their local presence and also produce 1° visualization grids. - Scale sensitivity: Over Europe, repeat analysis with refined 0.02° CFrC product using 7×7 (~14 km) and 17×17 (~35 km) windows to assess magnitude and pattern stability across scales. Validation and ancillary analyses: - Alternative time-difference method: Estimate CFrC change due to actual forest cover changes (pairwise year differences within 2004–2014), removing climate variability via local moving window residuals. Despite fewer samples and gradual land cover change, results broadly agree in sign with space-for-time estimates across climate zones. - Ground observations (SYNOP): Pair European stations differing in forest cover within 30–100 km, excluding water-influenced and complex terrain, and with sufficient 2004–2014 records. Associate forest fraction (30 m Global Forest Change) within 15 km radius. Regress CFrC differences against forest fraction differences to extrapolate to full transition (0%→100% forest), by hour and month; compare with satellite estimates near 14:00. - Energy balance context: Cross-compare estimated CFrC changes with co-located changes in net radiation (R), latent heat (LE), and sensible+ground heat (H+G) from prior space-for-time datasets, excluding strong snow-albedo cases, to interpret process covariations (recognizing timing and closure caveats).

- Global prevalence: For 67% of sampled areas, afforestation would increase low-level cloud fractional cover (CFrC). This rises to over 74% during May–September (seasonally aligned between hemispheres). - Magnitude and seasonality: Boreal summer shows widespread positive local increases of CFrC on the order of ~5% of the normal cloud fraction (relative) or about 0.03 absolute CFrC. Some regions (e.g., Indian subcontinent and Southern Africa) exhibit strongly seasonal relative increases reaching up to ~15%. - Regional patterns and inversions: Positive effects dominate warm seasons across temperate and many tropical/subtropical regions. Inversions occur in snow-affected winters/springs over North America, Russia, and Eastern Europe (less low clouds over forests vs open land). Non-snow inversions include the US Corn Belt in summer (more clouds over croplands), early monsoon India, and the southern Amazon dry season (enhanced clouds over deforested/fragmented landscapes). - Forest type dependence: Evergreen (needleleaf-dominated) forests generally induce stronger increases in low-level cloud cover than deciduous broadleaf forests across Europe. - Validation: European SYNOP stations show increased CFrC from April to August and during late morning to mid-afternoon, with seasonal patterns at ~14:00 consistent with satellite estimates (except negative values in February–March, likely influenced by fog reporting differences). - Process covariation: Where snow-albedo effects are minimal, increased CFrC tends to co-occur with higher net radiation after afforestation, and with increases in LE and/or H+G, consistent with combined roles of added moisture and enhanced buoyant lifting. Quantitative highlights: 67% global positive CFrC response (74% in warm season); typical relative summer increase ~5% (~0.03 absolute CFrC); seasonal peaks up to ~15% in some regions; strong needleleaf vs broadleaf contrast in Europe.

Findings indicate that afforestation generally enhances low-level convective cloud cover, implying additional cooling via increased planetary albedo and potential hydrological benefits. Mechanistic interpretations include: (1) Snow regimes: In snow-covered seasons, forests foster deeper, drier boundary layers and reduced fog/low clouds relative to adjacent snow-covered open lands, yielding negative CFrC changes; potential satellite cloud-snow confusion can accentuate this. (2) Water availability: Deep-rooted forests maintain transpiration in dry seasons enhancing clouds; during monsoon onset and extreme heat (e.g., India in May–June) or in intensively managed C4 croplands (US Corn Belt), non-forest vegetation can transiently produce more clouds. (3) Landscape heterogeneity: Fragmented or deforested mosaics with higher roughness can stimulate shallow convection, evident in parts of the Amazon dry season. (4) BVOCs and forest traits: High BVOC emissions (e.g., eucalypts, conifers) and needleleaf functional traits (greater roughness, lower albedo, higher Bowen ratio) may reinforce cloud formation over evergreen forests. Policy relevance: For land-based mitigation, the indirect cooling via enhanced low clouds likely complements carbon sequestration and can partly counteract surface darkening, particularly for conifers in Europe. Although winter snow reduces clouds and increases exposure to solar radiation, decreasing snow duration in a warming climate should strengthen the net cooling during higher-radiation seasons. Enhanced cloudiness may bolster precipitation and ecosystem resilience, supporting the terrestrial carbon sink. The results provide an observational benchmark to reassess model-based conclusions that emphasize albedo-driven warming from afforestation, especially concerning forest type choices in European policy. Caveats: The study quantifies local effects detectable at satellite overpass time; non-local teleconnections are not captured. Complex cloud-radiation feedbacks and precipitation responses require high-resolution coupled modeling to quantify radiative forcing and hydrological impacts.

Afforestation, forest restoration, and avoided deforestation are generally associated with increased low-level cloud cover worldwide, especially in warm seasons, indicating an indirect cooling effect aligned with carbon sequestration. Evergreen (needleleaf) forests often show stronger cloud enhancement than deciduous forests, suggesting that cloud albedo increases may offset part of their surface darkening. These cloud changes may also enhance regional water availability and ecosystem resilience. The observation-driven dataset offers a global benchmark to evaluate and constrain Earth system models and to inform nature-based climate strategies (e.g., European Green Deal). Future research should quantify the radiative forcing and precipitation impacts of afforestation-induced cloud changes, disentangle contributing processes (albedo vs. evapotranspiration vs. aerosols), assess non-local teleconnections, and examine interactions with irrigation, wetlands, and coastal influences using fine-scale coupled modeling and expanded ground observations.

- Attribution limits: Satellite observations alone cannot fully disentangle mechanisms (e.g., relative roles of albedo-driven buoyancy, transpiration, and BVOCs) or quantify top-of-atmosphere radiative forcing and precipitation responses. - Method scope: Space-for-time approach captures local effects; non-local/teleconnected impacts are not assessed. Lateral advection and averaging likely attenuate the observed magnitude, leading to conservative estimates. - Data constraints: Potential confusion between clouds and snow in satellite products; irrigation not explicitly separated in herbaceous class; wetlands/coastlines handled via masking but residual effects may remain; ground validation largely limited to Europe due to station density. - Temporal considerations: Alternative time-difference method has fewer samples and gradual land cover change may not fully express cloud regime shifts within 2004–2014. Energy balance comparisons are day-integrated and assumed to neglect feedbacks from cloud changes, complicating causal interpretation. - Topography and co-occurrence masks reduce spatial coverage; results depend on window size and scale (magnitude varies with resolution though patterns are robust).