Co-benefit of forestation on ozone air quality and carbon storage in South China

Domain: earth_science
Published: 2025-03-11
Editorial outcome: minor_revision

Abstract
Substantial forestation-induced greening has occurred over South China, affecting the terrestrial carbon storage and atmospheric chemistry. However, these effects have not been systematically quantified due to complex biosphere-atmosphere interactions. Here we integrate satellite observations, forestry statistics, and an improved atmospheric chemistry model to investigate the impacts of forestation on both carbon storage and ozone air quality. We find that forestation alleviates surface ozone via enhanced dry deposition and suppressed turbulence mixing, outweighing the effect of enhanced biogenic emissions. The 2005-2019 greening mitigated the growing season mean surface ozone by 1.4 ± 2.3 ppbv, alleviated vegetation exposure by 15%-41% (depending on ozone metrics) in forests over South China, and increased Chinese forest carbon storage by 1.8 (1.6-2.1) PgC. Future forestation may enhance carbon storage by 4.3 (3.8-4.8) PgC and mitigate surface ozone over South China by 1.4 ± 1.2 ppbv in 2050. Air quality management should consider such co-benefits as forestation becomes necessary for carbon neutrality.

Introduction
Green vegetation plays a pivotal role in the interaction between the terrestrial biosphere and atmosphere, affecting land-atmosphere energy and water exchanges and modulating the carbon and nitrogen cycle 1–3. It also serves as a major source of nonmethane volatile organic compounds (NMVOC) and depositing surfaces to take up air pollutants 4–6. Their density is measured by the leaf area index (LAI), defined as the one-sided green leaf area per unit ground area in broadleaf species and half of the total needle surface area per unit ground area in coniferous species 7,8. Recent satellite observations have revealed increasing trends in LAI over the globe, reflecting a generally greening Earth since 2000, despite ongoing deforestation in regions such as South America and Africa 9,10. China accounted for 25% of the global net increase in LAI during the period, mainly driven by forestation 10,11.

Forestation, including afforestation (planting new trees) and reforestation (replanting trees), is an important measure to enhance carbon storage in the biosphere and help achieve carbon neutrality 12–14. The recent Intergovernmental Panel on Climate Change (IPCC) report suggests that forestation should remove approximately one-quarter of atmospheric carbon to help limit global warming to 1.5 degrees 12,13. Sustainable forest management also helps achieve the United Nations Sustainable Development Goals (SDG) of food and clean water supply (SDG 2 and SDG 6) and ecosystem protection (SDG 15) 12,14,15. China has implemented multiple forestation programs since 1978 16, increasing forest stock volume (FSV) from 8.7 to 13.7 billion m3 over 1975–2005 17 (Supplementary Table 1). Afforestation is estimated to contribute over half of the increases in China’s total land carbon storage during the 2000s 18,19. As a necessary solution to protect ecosystems and to meet carbon neutrality, specific forestry development goals have been set in China 20, i.e., to expand the FSV by 6 billion m3 in 2030 relative to 2005 (13.7 billion m3) and further increase it to 26.5 billion m3 by 2050 (“Methods”).

Apart from the challenge of carbon neutrality, China still faces the air quality challenge, and greening can profoundly affect surface ozone air quality. Surface ozone is a major air pollutant in China and has continued to grow till recent years 21–24. Ozone is produced by the photochemical oxidation of carbon monoxide (CO) and NMVOC in the presence of nitrogen oxides (NOx = NO + NO2). Previous studies have demonstrated the complexity of biosphere-atmosphere interactions by accounting for counterfactual scenarios assuming changes in the land type and LAI, e.g., greening can change biogenic volatile organic compound (BVOC, biogenic NMVOC) emissions and hence ozone formation 25–27. Changes in the land type and LAI can also alter ozone dry deposition velocity, thus affecting ozone pollution and ozone-induced vegetation damage 28–30. A recent study found that forests can affect surface ozone concentrations by changing the vertical diffusivities and turbulent exchanges within and below the canopies 31,32. However, this canopy effect is rarely considered in current air quality models, and the net effects of historic actual greening in South China on ozone pollution remain to be determined.

In this work, we quantify the greening effects on carbon storage and ozone air pollution in South China over the past 15 years (2005–2019) and future projections under different shared socioeconomic pathways. We use a regional atmospheric chemistry model (WRF-Chem), driven by the satellite-measured LAI and land cover data, coupled with a forest canopy turbulence parameterization and an improved dry deposition scheme (Fig. 1). The improved model results are evaluated using ozone gradient measurements at a South China forest station. We demonstrate that future forestation-induced greening can benefit both carbon neutrality and ozone mitigation.

Results
Figure 2 shows the satellite observations of the growing season (April, July, and October) mean LAI and dense forest cover fraction over China since 2003. Significant increases in LAI have been observed in the past 15 years in South China, with 42% of the net increases attributed to forests and 32% to croplands 10. The regional mean LAI increased from 1.14 m2 per m2 in 2005 to 1.30 m2 per m2 in 2019 over China, while it increased from 2.38 m2 per m2 to 2.74 m2 per m2 over South China. Increases in annual mean LAI have also been reported in previous studies 9,10,33. Here, we find that the largest LAI increases occur in April, followed by October, which could be caused by an extension of the growing season in response to climate change over this period 34,35. LAI increases in July are small even though LAI peaks in the month (Supplementary Fig. 1). The satellite-observed land fractions of forest cover also increased steadily over this period (Fig. 2). The national mean dense forest cover fraction has increased from 9.8% in 2005 to 11.5% in 2019. The mean dense forest cover fraction in South China has increased from 18.6% in 2005 to 23.9% in 2019, and the increases in dense forests are associated with the decreases in savannas (Fig. 2 and Supplementary Fig. 2). The National Forest Inventory (NFI), which defines forests with the tree cover > 20%, shows a similar trend of 13% increases during this period (20.36% over 2004–2008 vs 22.96% over 2014–2018 in China) 17.

We apply the Weather Research and Forecasting model coupled with the Chemistry (WRF-Chem) regional air quality model to assess the impacts of greening on ozone air quality over southern China. A number of model improvements are implemented to better quantify the impacts of forestation by using satellite observations of LAI and major land cover fractions (Fig. 1; “Methods”). Recent newly conducted near-surface ozone measurements at the Dinghushan (DHS) Forest Ecosystem Research Station (a forest station in South China; Methods) reveal large ozone vertical gradients from above and below the forest canopy, and we find that capturing the near-surface ozone gradients requires coupling a forest canopy turbulence scheme and improving the dry deposition scheme to the regional air quality model (“Methods”; Supplementary Fig. 3). A series of simulations are conducted using the improved model for the growing season of 2019 (Table 1; “Methods”). As shown in Fig. 3, the baseline simulation well captures the nationwide measured surface ozone concentrations in the metric of maximum daily 8 h average (MDA8) with correlation coefficients of 0.7 and mean biases of within 12% in China.

Figure 3 shows that the 2005–2019 greening induces net decreases in surface MDA8 ozone by 1.4 ± 2.3 ppbv (mean ± standard deviation) over South China during the growing season. Large MDA8 ozone reductions of even over 8.0 ppbv occur over areas where the land cover switched from savanna land to forest (Supplementary Fig. 2). Ozone reductions are not confined in forested regions but are widely spread over South China. In addition to the human health metric MDA8, we find much greater effects on vegetation damage based on the ozone exposure metrics (AOT40, W126, and O3RH), gross primary productivity (GPP) reductions, and phytotoxic ozone dose (POD; “Methods”) 36,37. The monthly AOT40 and W126 values show high ozone exposure in North China, where ozone concentrations are particularly high, and considerable ozone exposure in South China (AOT40 of 2728 ± 2143 ppbv hour, W126 of 3098 ± 3022 ppbv hour) compared with other regions worldwide 21,37. The metrics of O3RH, GPP reductions, and POD show higher ozone exposure and vegetation damage in South China than in North China, as the low RH conditions in North China limit the stomatal uptake of ozone and the high vegetation abundance in South China 37.

The 2005-2019 greening can significantly reduce the exposure of vegetation to ambient ozone. We find that the average AOT40, W126, and O3RH in forests over South China decreased by 39%, 41%, and 15% during the growing season, respectively (Fig. 3 and Supplementary Fig. 4). However, the response of ozone-induced vegetation damage to greening effects differs depending on the metric chosen. The average GPP reductions related to ozone exposure in forests over South China decreased by 24% after considering LAI-induced GPP increases of 5% in 2019 compared to 2005, while the dose-based metric of POD1 increased by 10% due to increased stomatal uptake (Supplementary Figs. 4–5). This reverse response between the concentration metrics (AOT40, W125, O3RH, and GPP reductions) and the flux metric (POD1) is also measured by Ronan et al. 38.

The impact of forestation-induced greening on ozone air quality are complicated by the multiple potentially counteracting pathways of biosphere-atmosphere interactions. Here, as shown in Fig. 4, we diagnose the suppression of turbulence transfer below the forest canopy as the “canopy” effect, changes in dry deposition of ozone associated with LAI as the “deposition” effect, and changes in BVOC emissions due to greening as the “emission” effect. These effects can be separately quantified by model sensitivity simulations using fixed land cover data (i.e., using the 2005 level), fixed LAI in the model dry deposition scheme, and fixed LAI in the biogenic emission scheme (Table 1; Methods).

Figure 4 summarizes the statistics of the 2005–2019 ozone changes in South China due to greening via different effects averaged over the growing season. Supplementary Fig. 6 shows their spatial and temporal variations. For the regional means, the 2005–2019 greening has reduced the mean surface MDA8 ozone concentrations in South China by 0.7 ppbv (-0.3–4.5 ppbv; 25–75% percentiles) via the “canopy” effect and by 1.0 ppbv (0.4–1.5 ppbv) via the “deposition” effect, while the “emission” effect could have slightly increased the mean MDA8 ozone by 0.3 ppbv (0.2–0.5 ppbv). The “canopy” and “deposition” effects exceed the “emission” effect, leading to overall net decreases in surface MDA8 ozone levels as induced by the 2005–2019 greening in South China.

Substantial differences in spatial and seasonal patterns are found for the three effects (Supplementary Fig. 6). The “canopy” effects tend to be localized, lowering MDA8 ozone values by up to 10 ppbv in forestation areas, but raising them by up to 10 ppbv in deforestation areas and by ~1.3 ppbv in urbanization areas (Supplementary Fig. 2). The ozone changes due to “deposition” and “emission” effects in South China are less localized compared to the “canopy” effect and heavily rely on LAI changes. Different from the “canopy” effect, which has almost no seasonal changes, the “deposition” and “emission” effects show large seasonal variations as driven by seasonal changes in LAI (Supplementary Fig. 6). Greening, as represented by LAI increases, would provide larger depositing surfaces to weaken resistances of stomatal and cuticular removal and thus enhance ozone dry deposition velocities over South China by ~2% in July and up to 9% in April (Supplementary Fig. 7). Meanwhile, the 2005–2019 LAI increases further enhanced emissions of BVOC by 4% in July and 20% in April in South China. The resulting monthly mean surface MDA8 ozone changes in South China associated with the 2005–2019 greening are -0.3~-1.7 ppbv due to the “deposition” effect and 0.1–0.4 ppbv due to the “emission” effect. A previous study reported up to 0.5 ppbv reduction in surface ozone from 1992 to 2014 due to dry deposition enhancements and small isoprene emission increases over southern China 39, consistent with our results.

Our analyses further emphasize the important role of the “canopy” effect. The forest can suppress turbulence and vertical transport below and near its canopy, thus reducing ozone from the free troposphere mixing down to the surface. Such a turbulence suppression effect can be captured by our improved regional air quality model when coupled with the forest canopy turbulence scheme. In the presence of the forest canopy, the regional mean near-surface eddy diffusivity coefficients over South China decreased by 31% in April 2005 and by 41.7% in April 2019 compared with the conditions in the absence of the forest canopy effect (Supplementary Fig. 8). The presence of the canopy turbulence effect leads to a sharp gradient of ozone near the surface, as recorded by the ozone gradient measurements at DHS and simulated by the model, and reduces the mean surface MDA8 ozone concentrations by 0-12.6 ppbv over South China in April 2019 (Supplementary Figs. 3 and 8). Meanwhile, it increases ozone concentrations above the canopy and redistributes the vertical ozone profile in the lower troposphere. The 2005–2019 forestation in South China has led to about 35% stronger turbulence suppression by the forest canopy (Supplementary Fig. 8), which largely drove the “canopy” ozone effect shown in Fig. 4. Forest canopy expansion would also induce a modest increase in ozone dry deposition velocity via increases in forest land cover, while we find that such an effect on ozone pollution is much smaller than the suppression of turbulence (Supplementary Figs. 6, 7).

As a potential solution to facilitate carbon neutrality, future forestation could further green South China and may impact both ozone air quality and carbon stocks. Previous forestation goals have been achieved in China’s five-year plans. China has now announced an increase in FSV to 19.7 billion m3 by 2030 and to 26.5 billion m3 by 2050, based on which we can estimate future increases in forest LAI and carbon stocks (Methods). We find a strong linear relationship between the FSV and the forest LAI using reported historical statistics (Supplementary Fig. 9). Such a relationship can be applied to estimate future LAI given the projected FSV. We estimate that the annual mean forest LAI will increase by 8.1% in 2030 and by 34% in 2050 relative to the 2019 condition in South China (Methods; Fig. 5). The largest increase in LAI is projected to occur in the evergreen broadleaf forests of southwestern China. The estimated maximum LAI is 7.7 in 2030 and 14.9 in 2050, within the range of historically measured high values 40,41.

Forest carbon stock (also called carbon storage) is a commonly used metric to represent carbon stored in natural forests. It can be calculated by the product of FSV and the biomass expansion factor (BEF, defined as the ratio of forest biomass to timber volume) 42,43. Based on previous estimates of Chinese forest biomass carbon stocks (Supplementary Table 2), we derive a national mean BEF of 0.96 tons per m3 with a range of 0.85–1.07 tons per m3 (Methods). The forest biomass carbon stocks are thus estimated to be 6.6 PgC (5.8 to 7.3 PgC) in the year 2005, 8.4 PgC (7.4 to 9.4 PgC) in 2019, increasing to 9.4 PgC (8.3 to 10.5 PgC) in 2030, and further to 12.7 PgC (11.2 to 14.2 PgC) in 2050. Our estimate of future forest biomass carbon stocks is in line with previous data-driven studies (8.0 to 10.8 PgC in 2030 and 9.0 to 13.1 PgC in 2050) 44–46.

In addition to carbon uptake, future greening would have a co-benefit on surface ozone air quality as driven by biosphere-atmosphere interactions. To assess such impacts on future ozone air quality, we conduct model sensitivity simulations with projected LAI over China in 2030 and 2050 (Table 1). Future anthropogenic emissions are projected in different shared socioeconomic pathways (SSPs) reported by the IPCC. We use the future (2030) anthropogenic emissions by a low-emission scenario (SSP126) and a high-emission scenario (SSP370) in 2030 (Methods). In the SSP126 scenario, Chinese NOx emissions would decrease by 31%, and NMVOC emissions would decrease by 42%, while in SSP370, Chinese NOx and NMVOC emissions would increase by 38% and 24%, respectively, during the period from 2019 to 2030. Future (2030) anthropogenic emissions changes alone would decrease the mean surface MDA8 ozone concentration by 4.3 ppbv under the SSP126 scenario, while they would increase it by 3.7 ppbv under the SSP370 scenario in South China (Fig. 5 and Supplementary Fig. 10).

We find that, for all three anthropogenic emission scenarios (the 2019 condition, SSP126, and SSP370), the 2019–2030 forest greening would lead to decreases in surface ozone levels. As shown in Fig. 5, accounting for the 2019–2030 forest greening would decrease the mean MDA8 ozone in South China by 0.36 ppbv (0.01–0.71 ppbv) under the 2019 emission condition, 0.34 ppbv (0.01–0.67 ppbv) in SSP126, and 0.40 ppbv (0.01–0.79 ppbv) in SSP370. The greening effects are slightly higher in SSP370 due to the high NOx emission levels in the scenario. 2019–2030 anthropogenic emission increases in the SSP370 scenario would increase the mean ozone by 3.7 ppbv in South China; however, if we consider the impacts of future greening, such increases would be reduced by 11%. As the forest LAI increases extending to 2050, the mean surface MDA8 ozone concentrations would be reduced by 1.4 ppbv (0.2–2.6 ppbv) over South China compared to the 2019 level (Fig. 5). Furthermore, future forest greening would decrease ozone-induced GPP damage by 0.5% ± 2.9% in 2030 and 4.9% ± 6.7% in 2050 though vegetation abundance increases (Supplementary Fig. 5). These ozone benefits occur mainly in South China, where we project the most forest LAI enhancements (Supplementary Figs. 9, 10). Such benefits may only present a conservative estimate, as here, the “canopy” effect that could further reduce near-surface ozone is not accounted for by only accounting for future LAI increases. Future forest land cover increases, which are highly uncertain, can further help ozone air pollution mitigation.

Forestation thus offers a win-win strategy for ozone air pollution abatement and carbon neutrality. The complex biosphere-atmosphere interactions associated with forestation, as quantified in this study, can also be important in many other regions over the globe, such as Brazil, India, Mexico, and tropical African countries, where significant forestation has occurred in recent decades (Supplementary Fig. 11). These regions are also facing increasing tropospheric ozone levels as seen by satellite observations 47, and have committed nearly 300 Mha of degraded land aiming to restore 350 Mha of the forest by 2030 (www.bonnchallenge.org) 13. In addition to the benefits for carbon storage and ozone air quality, forestation can also contribute to ecosystem services, biodiversity protection 48, and water management 49, requiring integral consideration in environmental strategies in future work.

Our analyses include some uncertainties. First, a number of global LAI products based on different satellite instruments and inversion algorithms provide a wide range of leaf area estimates. Most of these LAI products recorded an increasing trend over South China in recent decades, and the MODIS estimate of a 15% increase in LAI over 2005–2019 is in the middle of the various products 10,33,50 (Supplementary Information). Second, land cover changes, such as forest expansion, would affect local surface albedo, energy balance, and meteorological conditions 51,52. Forestation of dryland regions was recently found to have a large warming potential due to the reduced albedo effect 53. Forestation in South China is mainly converted from savannas. This may cause small decreases in albedo while increasing latent heat near the surface, leading to slight decreases in surface temperature, and thus the effect on ozone formation can be small (Supplementary Fig. 12). Our model simulations by nudging the meteorological conditions every two days can account for the diurnal meteorological responses due to the land cover changes while excluding long-term and large-scale atmospheric circulation changes. In a model simulation without meteorological nudging, we find that the surface ozone changes due to the 2005–2019 forestation greening would be ~6% larger (Supplementary Fig. 13). Furthermore, recent studies have demonstrated strong positive ozone-vegetation feedbacks where ozone-induced vegetation damage and inhibition of stomatal conductance would enhance ozone air pollution 26,54,55. Addressing such feedback requires online coupling of an air quality model with a land vegetation model, which is not accounted for in the study. We expect that surface ozone benefits due to forestation would be enlarged when considering reduced ozone levels to further lower vegetation damage.

Methods
Satellite observations of leaf area index and land cover
Land type and vegetation information are from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on NASA’s Terra and Aqua satellites. The 8-day composite leaf area index (LAI) product (MCD15A2H Version 6) with a 500m spatial resolution, the monthly normalized difference vegetation index (NDVI) product (MOD13C2 Version 6.1) with a 0.05° resolution, and the land cover type product (MCD12Q1 Version 6) with a 500m resolution are used in this study 56–58. We filter out LAI and NDVI data under cloudy conditions and replace invalid values with valid ones nearest in time to improve data coverage 59,60. The LAI dataset is averaged monthly, and then a 24-month running average is applied to identify the long-term trend. The LAI and NDVI datasets are then spatially aggregated to a 27 km resolution for model inputs. The monthly mean LAI and NDVI datasets from 2003 to 2020 are calculated and utilized in this study.

The MCD12Q1 dataset contains 17 land cover categories following the International Geosphere and Biosphere Program (IGBP) 61. We reproject the tiled MCD12Q1 dataset from 2003 to 2019 and spatially aggregate it to generate yearly land cover model inputs that include the major land cover types and fractions for the 17 categories at 27 km resolution. The IGBP classification types are further grouped into four plant functional types (broadleaf tree, needleleaf tree, shrub/brush, and herb/crops/grasses) for the calculation of biogenic emissions. We define forest grid cells when the total forest land cover fractions (including evergreen needleleaf, evergreen broadleaf, deciduous needleleaf, deciduous broadleaf, and mixed forest as categorized in MCD12Q1) are higher than 20%. It should be noted that forest land cover types in MCD12Q1 IGBP represent dense forests with tree cover >60% and canopy height >2m. This differs from NFI, which defines forests with tree cover >20% 17.

Observations of air pollutants and meteorology
Ozone concentration gradients were measured at a flux tower in the Dinghushan (DHS) Forest Ecosystem Research Station (a forest station in South China; 112°32′28′′E, 23°10′25′′N) from 21 August to 6 September 2019 62. The flux tower is located in the central DHS and is surrounded by coniferous and broad-leaved mixed woodland. The ozone concentrations are concurrently measured on the tower at 7m and 30m above the surface using two closed-path eddy covariance ozone flux measurement systems. We calculate ozone concentration differences at the two levels as the 7m–30m ozone gradients. More details on the DHS and ozone measurements are given in Liu et al. 63. Ozone measurements at five background stations in China have accessed from the Tropospheric Ozone Assessment Report (TOAR) dataset (https://doi.org/10.34730/19cdb0ca3ad046bd88449f38b426542b).

Nationwide hourly measurements of surface ozone concentrations at over 300 cities in China for 2019 are obtained from the China Ministry of Ecology and Environment national network (MEE, http://106.37.208.233:2035/). Observations of the tropospheric formaldehyde (HCHO) column in 2019 retrieved from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S5P) satellite are used to evaluate BVOC emissions in the model. BVOC emissions are the dominant source of HCHO in South China 64,65 and have proven to be strongly correlated with the satellite HCHO vertical column in the region during the summer months 65,66. We filter out the HCHO column under cloudy conditions and only use the valid data with a QA > 0.5 67,68.

In addition, meteorological observations, including 10-m wind speed (WS10), 2-m air temperature (T2), and 2-m dew-point temperature (Td2) at 366 stations over China in the growing season of 2019 are collected from the National Climatic Data Center (NCDC, https://ncdc.noaa.gov/isd/data-access). Dew-point temperature observations are converted to relative humidity (RH) according to the Tetens empirical formula. Evaluations of model-simulated meteorology with these observations generally show good agreement (Supplementary Table 3).

These available observations are used to assess the performance of the air quality model, employing various statistical metrics such as mean values (OBS and MOD), mean normalized bias (MNB), normalized mean bias (NMB), correlation coefficient (R), and the index of agreement for validation (IOA).

Ozone metrics relevant to air quality and vegetation damage
We calculate the maximum daily average 8h (MDA8) ozone, three exposure-based vegetation ozone metrics (AOT40, W126, and O3RH), and one dose-based vegetation ozone metric of phytotoxic ozone dose (POD1). These metrics are commonly used to quantify ozone air pollution and its impacts on human health and vegetation 36,37. The MDA8 ozone is the metric used by the WHO air quality guideline and the air quality standard in China. The mean MDA8 ozone values (in the unit of ppbv) in the growing season are used to estimate long-term ozone exposure in this study. The monthly AOT40 (accumulated hourly ozone concentrations over a threshold of 40 ppbv during 08:00 to 19:59; in the unit of ppbv hour) and the monthly W126 (the sigmoidal weighted sum of all hourly ozone concentrations during 08:00 to 19:59; in the unit of ppbv hour) metrics consider the impacts of cumulative ozone concentrations on vegetation. The metric of O3RH (in the unit of ppbv hours) further accounts for the influence of ambient RH on ozone stomatal uptake and is estimated as MDA8 over 20 ppbv multiplied by RH between 40–80% 37. The metric of POD1 (in the unit of nmol m-2 s-1) further considers vegetation damage as ozone uptake fluxes entering stomata rather than ozone exposure and is estimated as accumulated hourly stomatal ozone flux (Fs,O3) that exceeds a threshold of 1 nmol O3 m-2 s-1 during 08:00 to 19:59 38. We calculate monthly AOT40, monthly W126, daily O3RH, and monthly POD1 using the equations below.

WRF-Chem model configuration
The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.6.1 is employed to simulate ozone air pollution. The model domain covers China and its adjacent areas with a 27 km spatial resolution. It has 38 layers vertically from the surface to 50 hPa, with the first and second layers centered at 10m and 30m above the ground, respectively. The initial and lateral meteorological boundary conditions use the National Center for Environmental Prediction Final (FNL) Analysis data with 1° spatial resolution and 6-hour temporal resolution. To avoid drifting in simulated meteorology, we reinitiate meteorological fields with the FNL Analysis every two days. The chemical initial and boundary conditions are provided by the CAM-Chem simulation output 69. We use the gas-phase Carbon-Bond Mechanism Z mechanism 70 to simulate tropospheric ozone chemistry. Dry deposition and vertical mixing processes are calculated by Wesely’s dry deposition scheme 71, the Unified Noah land-surface model 72, and the Yonsei University planetary boundary layer scheme 73. Other physical and chemical parameterization schemes follow Liu et al. 74.

For emissions in the model, the baseline simulation uses the 2017 Multi-resolution Emission Inventory for China (MEIC) and the 2010 MIX inventory for regions outside China (www.meicmodel.org/), except for Chinese agricultural ammonia (NH3) emissions from Zhang et al. 75 with updated statistics for 2017. Biomass burning emissions use the Fire Inventory from the NCAR 76. Biogenic emissions are calculated online using the Model of Emissions of Gases and Aerosols from Nature 77 (MEGAN) with improved model inputs of LAI and land type described below. The projections of future anthropogenic emissions are based on two shared socioeconomic pathways (SSPs; SSP1-sustainability and SSP3-regional rivalry) with different 2100 forcing levels (2.6 W m-2 and 7.0 W m-2) accessed from Coupled Model Intercomparison Project Phase 6 (CMIP6; https://esgf-node.llnl.gov/projects/input4mips/). The 2015–2030 emission trends in a low-emission scenario (SSP126) and a high-emission scenario (SSP370) are then applied to the 2015 MEIC emissions to produce two sets of hypothetical 2030 Chinese anthropogenic emissions.

Model improvements for the simulation of land-atmospheric interactions
Coupling a forest canopy turbulence parameterization. Forest would suppress vertical turbulence diffusivity within and below its canopy; however, this effect is not accounted for in default WRF-Chem. Here we implement a forest canopy parameterization in the boundary layer mixing scheme of the model. The parameterization modifies the turbulence diffusivity and then the vertical mixing of atmospheric chemical constituents (all gaseous and aerosol species) within the forest canopy, following a localized-near-field approximation proposed by Raupach 78. It has been recently applied by Makar et al. 32, which demonstrated the large effects of the forest canopy on turbulence mixing and surface ozone levels over North America. This Eulerian-based modeling approach with first-order closure K-theory is suitable for WRF-Chem since it is more numerically efficient than the higher-order closure approach and the Lagrangian-based stochastic modeling approach 79,80. Following Makar et al. 32, the parameterization can be expressed by the formulas below to estimate the suppression of vertical diffusivity (K) within and below the canopy.

The forest canopy height Hc is computed from the satellite dataset of NDVI by using the equation 81.

Accounting for updated LAI and land cover fractions in land-atmosphere exchanges. The default WRF-Chem model only considers the dominant land cover type in each grid cell in the calculations of land-atmosphere exchange and dry deposition. Here, we implement a mosaic approach into the Unified Noah land-surface scheme following Li et al. 82. We mosaic each model grid with the land cover fractions of five major land types from the MODIS dataset. The land-surface exchanges in each model grid are calculated for five major land types and then averaged with land type fractions as weights. The total fractions of the five major land cover types are above 99% in most grid cells. The same mosaic approach is applied in the model dry deposition scheme so that the effects of changes in land cover types on dry deposition can be more accurately represented in this study. We further improved the model dry deposition calculation by accounting for the dependence of canopy stomatal and leaf surface resistances on LAI 83. In addition, biogenic emissions estimated by the MEGAN algorithm require inputs of LAI and plant functional types (PFTs). These model inputs are now updated using the MODIS satellite datasets as described above. Estimated biogenic isoprene emissions in China are 0.65, 2.07, and 0.58 Tg C in April, July, and October 2019, respectively, in line with previously reported values 65,84,85. The resulting model simulated HCHO column concentrations are in good agreement with satellite observations over South China (Supplementary Fig. 14).

The improved model simulation is supported by surface ozone measurements at the five background sites in China (Supplementary Fig. 15). In particular, the large daily mean near-surface (7m/30m) ozone vertical gradient of 6.3 parts per billion by volume (ppbv) measured at DHS and surface ozone concentrations at Shangri-La (XGLL, a forest background station) can now be well captured in the baseline simulation (Supplementary Figs. 3 and 15, and Supplementary Table 4).

WRF-Chem model sensitivity simulations
We conduct WRF-Chem model simulations for three months (April, July, and October) of 2019 each after a 3-days spin-up for initialization due to limited computing resources. We average values of the three months as the growing season mean. Four sets of model simulations are conducted to examine the impacts of greening from 2005 to 2019 and future forestation from 2019 to 2030 and 2050 on ozone air quality in China (Table 1).

First, the Base simulation uses the model configurations described above and applies MODIS LAI, NDVI, land cover datasets, and anthropogenic emissions for 2019. The baseline simulation represents the present-day condition that can be evaluated with observations. Second, a sensitivity simulation replaces LAI, NDVI, and land cover data with 2005 conditions. The differences in ozone concentrations with Base are then used to estimate the overall effects of greening from 2005 to 2019. Third, a set of sensitivity simulations are performed by (1) fixing NDVI and land cover data to the year 2005 conditions, (2) fixing LAI in dry deposition for the year 2005, and (3) fixing LAI and PFT in MEGAN for the year 2005. Their differences with the Base simulation thus quantify, respectively, the “canopy,” “deposition,” and “emissions” effects. The “canopy” effect of greening is attributable to forest canopy height and land cover changes, as determined by NDVI and land cover data. The “deposition” effect of greening is attributable to changes in dry deposition associated with LAI. The “emission” effect of greening is attributable to changes in BVOC emissions associated with LAI and PFT. It should be noted that the “canopy” effect diagnosed here includes not only changes in turbulence mixing due to forestation but also changes in dry deposition and surface energy balance due to changes in land type, while we find the latter to be small for ozone air quality. The overall greening effects slightly differ from the sum of “canopy”, “deposition”, and “emissions” effects due to nonlinear behaviors. Fourth, a group of sensitivity simulations is conducted by (1) replacing LAI with the 2030 condition (denoted LAI2030), (2) using 2030 future anthropogenic emissions (denoted SSP126 and SSP370), (3) replacing both LAI and anthropogenic emissions with 2030 conditions (denoted SSP126LAI2030 and SSP370LAI2030), and (4) replacing LAI with the 2050 condition (denoted LAI2050). Their differences with the Base simulation reflect, respectively, (1) the effects of greening in 2030, (2) the effects of 2030 future anthropogenic emission changes, (3) the effects of 2030 greening and anthropogenic emission changes jointly, and (4) the effects of greening in 2050 over China. In all simulations, we hold other factors such as meteorology and CO2 levels constant, allowing us to isolate the effects of greening on ozone.

Ozone-induced GPP damage
We apply an offline version the Yale Interactive Terrestrial Biosphere (YIBs) model, a process-based vegetation model, to evaluate the impacts of ozone-induced vegetation damage on gross primary productivity (GPP) in China. The YIBs model simulates photosynthesis with the Michaelis–Menten enzyme-kinetics scheme, the stomatal conductance with the model of Ball and Berry, and the ozone damage ratio with the semi-mechanistic parameterization to generate the GPP and ozone-induced GPP changes 86. The YIBs model has been verified thoroughly with site-level measurements and satellite retrievals 86. We performed the YIBs model across China with a 0.25° spatial resolution in 2019 after a 4-year spin-up for initialization. The model was driven by meteorological fields from Version 2 of Modern Era Retrospective-analysis for Research and Application (MERRA2), and WRF-Chem simulated ozone concentrations. Our estimates of GPP after considering ozone-induced damage are, in general, consistent with the MODIS product (MOD17A2HGF Version 6) across China in 2019 (Supplementary Fig. 16).

Forestation-induced GPP increase
We estimate forestation-induced GPP increases using the light-use efficiency approach, which is widely used in satellite-based studies 87–89. This approach decomposes GPP into the fraction of absorbed photosynthetic active radiation (fAPAR), photosynthetic active radiation (PAR), and light use efficiency (LUE). fAPAR can be evaluated via Lambert-Beer’s law.

Future LAI in 2030 and 2050
China plans to enhance its forestry development in the future through technological advancements and phased forestry goals. We obtain a conservative estimate of the Chinese LAI in 2030 by forestation according to the goal of increasing the national forest stock volume (FSV) by 6 billion m3 in 2030 relative to 2005 (13.7 billion m3) to peak CO2 emissions before 2030 and to achieve carbon neutrality before 2060 (http://english.mee.gov.cn/News_service/media_news/202012/t20201214_812718.shtml, proposed at the Climate Ambition Summit). We also estimate an ambitious LAI based on the goal of expanding FSV to 26.5 billion m3 by 2050 for forestry development proposed at the 19th National Congress (https://bioenergyinternational.com/policy/china-sets-ambitious-forestry-development-goals-2050). Using historical FSV statistics from the NFI yearbook (Supplementary Table 1), we find that mean forest LAI is linearly correlated with FSV over China. Thus, we forecast that the mean forest LAI in 2030 (2050) would increase to 2.16 ± 0.02 m2 per m2 (2.68 ± 0.05 m2 per m2) (Supplementary Fig. 9). Here, we assume that the future LAI increments relative to the 2019 LAI follow the spatial distribution of changes in forest cover fractions during 2005–2019 (Supplementary Fig. 9).

Forest biomass carbon stocks
The biomass expansion factor (BEF, defined as the ratio of forest biomass to timber volume) method is applied to estimate forest stand biomass 42,43. On a national scale, total forest biomass can be calculated as:

Using FSV from the forest inventory dataset (Supplementary Table 1) and estimates of Chinese forest biomass carbon stocks in the literature (Supplementary Table 2), we derive a national BEF value of 0.84–1.07 tons per m3 over China. Previous studies reported BEF values ranging from 0.69–1.47 for China’s major forest types 43,90,91. We estimate future Chinese forest biomass with the BEF value and future FSV and then convert it to the forest biomass carbon stock with a conversion ratio of 0.5.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The article by Liu et al. uses a modeling approach to explore the role of greening (through forestation) and future emissions on atmospheric ozone and the ozone effect on vegetation. There is a lot here, and I think presented in an overly complex style that is difficult to get through. This article reads like an entire thesis was compressed into a single paper and the bulk of the figures thrown into the supplement. It really needs to be rewritten and presented in a more easily consumable matter before publication in a journal like Nature Communications I think.\n\nThe central argument is that models need to account for the effects of forests on ozone properly in order to determine ozone levels in the atmosphere (which affect human health), effect of ozone on vegetation, and carbon storage of the resulting vegetation. They basically use a modeling approach with improvements to the effect of turbulence on vertical ozone distribution in the forest canopy, dry deposition of ozone, and also include emissions of BVOCs (there should also be a clearer distinction with NMVOCs), which contribute to ozone production. A central argument from the experiments is that turbulence and dry deposition reduce the amount of ozone in the atmosphere, and more than offset any increases from the BVOCs (thus putting to rest the old political argument that trees pollute). However, I am not clear on how more dry deposition helps reduce the effect of ozone on vegetation and adds to greening – which I think, but am not sure, is what they are arguing? That would seem to have the reverse effect, as the ozone that has been removed from the atmosphere is now going to limit the greening through stomatal uptake. There needs to be a better explanation of each of these effects up front – not just references to the literature. At least a couple of sentences making clear the effect of each process on both atmospheric ozone levels and the effects on vegetation.\n\nThere are 11 experiments as outlined in Supplementary Table 6 (maybe this table should be in the main text as it is kind of crucial). There is a paragraph in the Methods (lines 495-516) explaining these experiments, including text outlining what the differences with the Base represent. There should be another table or column highlighting these effects, as it is difficult going through the results with references to each of the experiments and no clear guidance on what they each represent. Also, the discussion of some of the processes, like future LAI, comes after the Table, so at the time of reading, it doesn’t make a whole lot of sense. There needs to be a clearer explanation of why land cover and NDVI are important for turbulence, LAI for deposition, and LAI and PFT important for emissions (with a note that MEGAN is used for emissions). The future LAI itself essentially comes from bookkeeping methods. What if they used LAI predicted from one of the CMIP6 models related to the two SSP scenarios (SSP126 and SSP370) – how does that, from multi-models, compare to the assumptions they make here about LAI?\n\nHow does Base – pregreening experiments isolate the effect of ozone on vegetation? Between 2005 and 2019 climate is changing, CO2 is increasing, N deposition is presumably increasing – so while greening has increased, and that does have an effect on reducing the ozone, what have they done here to isolate the effect of the ozone? I am unclear in these experiments between 2005 and 2019 how they have accounted for these other factors. I guess the argument is that by accounting for greening they are accounting for these factors, but what about the effect of greening on N deposition, for example (and how that would affect ozone chemistry in the atmosphere) – wouldn’t that counteract the effect of the ozone? It seems to me there should be some experiments with and without the ozone effect itself. So there needs to be an explanation of how greening accounts for that and why no explicit ozone experiments are required.\n\nThere are a lot of models and datasets used in this study. It would be useful to have a flow chart diagram that shows how all the different models and datasets are interconnected, and where the improvements are made.\n\nSome of the map figures comparing model to observations (e.g. Fig. S4a, Fig. 2a,b – why doesn’t b have those inset numbers?) I think are showing the observations and using the inset numbers to show how the model compares over the whole of China. If that’s the case, it would be better to show difference maps so the regional differences between model and observations are clearly visible.\n\nThe overall conclusion is that the effect of the greening in reducing ozone surpasses even potential ozone increases from the emission scenarios (i.e. SSP370) themselves. How much of the improvements in air quality is simply less ozone at the very surface, and more just above the canopy? I supposed that would improve human health, but the ozone is still there – either damaging the vegetation or in the atmosphere, still acting as a greenhouse gas, for example.", "reviewer_2": "The paper by Liu and colleagues describes simulations of O3 changes associated with increasing forestation in south China. They describe improvements to the surface turbulence parameterization in the WRF-Chem model and they perform sensitivity tests involving changes in forest area, canopy height, leaf area index over the period 2005-2050. One of their main conclusions is that increasing forest area can decrease near-surface O3 in addition to storing carbon. Since it is well-known to atmospheric chemists that vegetation decreases near-surface O3 via dry deposition (and reaction with BVOCs), the strength of this work is in quantifying both effects together, the co-benefit of forestation.\n\nMy main critique of the paper is the way that the turbulence parameterization is implemented in the model and then tested. The turbulence parameterization was previously designed (Maker et al., 2017) to simulate vertical turbulence within the forest canopy of a model with multiple layers of canopy vegetation. The method section of this paper, however, does not mention a multi-layer canopy, so I presume that the authors are using the typical single-layer canopy configuration in which all deposition is assumed to occur at the model surface. In that case, I do not think that applying this in-canopy turbulence parameterization is appropriate.\n\nThe evaluation of simulated O3 concentration gradients at DHS appears inconsistent with the way observations were collected. At the DHS site, O3 was measured above and within the forest canopy. Thus, part of the O3 gradient is caused by O3 deposition to the upper canopy between the two measurement heights. As mentioned, with the single-layer canopy model, however, all deposition is assumed to occur at the model surface, so that near-surface gradients may be calculated by assuming a constant-flux layer. It thus appears that the model has been induced to match the gradient by excessively reducing the vertical turbulence, rather than by accounting for the vertical distribution of O3 deposition within the canopy. I am thus unpersuaded by all of the conclusions related to how canopy turbulence affects O3 (lines 110-124, lines 197-212, abstract).\n\nIt is confusing to call sensitivity test 1 “turbulence” since dry deposition changes as well. The authors acknowledge this in the methods, but the rest of the paper is written as though test 1 only changes turbulence and not deposition.\n\nI would like to see a discussion of whether it is possible to double FSV and more than double LAI by 2050 within the constraints of projected forest area and the known species found in these regions. If not, the future scenario is fanciful.\n\nI have several minor comments as well.\nL34: “shall” has a legal meaning, which is not applicable here. I suggest “should”\nL42: Unclear what “their abundance” refers to.\nL45-46: There are also large areas of deforestation and declining LAI, so the global trend is not uniform, which I think should be reflected here.\nL47: “Greening” is not a specific technical term, so it’s not clear what 25% refers to without reading the cited works.\nL52: Sentence unclear.\nL458. Are the maximum and minimum canopy heights prescribed as two values for the entire model domain or spatially variable?\n\nIt’s not clear whether the parameterization of turbulent diffusion within the canopy (p12) is applied only to O3, or also to water vapor, other gases, heat, and momentum. For consistency, all of these should be affected.\n\nWhat role do crops and agriculture plan in the LAI trends. Changes to crop types, increasing crop cycles, and management practices could also contribute to the LAI trends.\n\nFig 1 shows large increases in LAI in regions that do not have increases in dense forest cover (e.g. Yunnan, Hunan, Jiangxi provinces).\n\nL182 says that the changes follow “normal distributions” but I do not see any evidence of a gaussian functional shape. This should be revised.\n\nFuture increases in forests should be described as a possibility, not a certainty, in the section beginning on line 214."}, "author_response": "Point-by-Point Response Letter for Nature Communications manuscript NCOMMS-24-19589\n“Co-benefit of forestation on ozone air quality and carbon storage in South China”\nResponse to Reviewers\nWe thank the editor and all reviewers for their valuable time and comments. All comments and suggestions are carefully analyzed and considered. The following is a point-by-point response to the reviewers' comments, with changes to the original manuscript clearly indicated. Our response, highlighted in blue text, provides the necessary clarifications and details the specific actions we've taken to improve the manuscript. The revisions made to the original paper are indicated in green text.\n\nReviewer #1 (Remarks to the Author):\nComment [1-1]: The article by Liu et al. uses a modeling approach to explore the role of greening (through forestation) and future emissions on atmospheric ozone and the ozone effect on vegetation. There is a lot here, and I think presented in an overly complex style that is difficult to get through. This article reads like an entire thesis was compressed into a single paper and the bulk of the figures thrown into the supplement. It really needs to be rewritten and presented in a more easily consumable matter before publication in a journal like Nature Communications, I think.\nResponse [1-1]: Thank you for the comments. We have further improved our manuscript based on all your suggestions. We appreciate your comment about our efforts for the integrity of this work. We aim to conduct a more comprehensive analysis of how greening through various processes affects surface ozone levels.\nIn revising the main text, we have relocated some detailed methodological descriptions to the Methods section and streamlined the content. The figures and tables in the supplement provide important details that are not covered in the main text, so we recommend keeping them but have organized them in a more logical flow. Additionally, to enhance readability, we have included a framework diagram and a table summarizing conducted sensitivity tests in the text.\n[Ok, it does seem more readable – however, not sure what was moved into the Methods that was not previously there based on the tracked changes.]\n\nComment [1-2]: The central argument is that models need to account for the effects of forests on ozone properly in order to determine ozone levels in the atmosphere (which affect human health), effect of ozone on vegetation, and carbon storage of the resulting vegetation. They basically use a modeling approach with improvements to the effect of turbulence on vertical ozone distribution in the forest canopy, dry deposition of ozone, and also include emissions of BVOCs (there should also be a clearer distinction with NMVOCs), which contribute to ozone production. A central argument from the experiments is that turbulence and dry deposition reduce the amount of ozone in the atmosphere, and more than offset any increases from the BVOCs (thus putting to rest the old political argument that trees pollute). However, I am not clear on how more dry deposition helps reduce the effect of ozone on vegetation and adds to greening – which I think, but am not sure, is what they are arguing? That would seem to have the reverse effect, as the ozone that has been removed from the atmosphere is now going to limit the greening through stomatal uptake. There needs to be a better explanation of each of these effects up front – not just references to the literature. At least a couple of sentences making clear the effect of each process on both atmospheric ozone levels and the effects on vegetation.\nResponse [1-2]: Thanks for the comment. We fully understand your concern about dry deposition and ozone-vegetation feedback. Our paper aims to explore the processes through which greening affects surface ozone. While we acknowledge that ozone can also damage vegetation, this feedback is not included in the study. We present a discussion about ozone-vegetation feedbacks in Line 301-306 (Page 8-9) in the revised manuscript: “Furthermore, recent studies have demonstrated strong positive ozone-vegetation feedbacks where ozone-induced vegetation damage and inhibition of stomatal conductance would enhance ozone air pollution26,54,55. Addressing such feedback requires online coupling of an air quality model with a land vegetation model, which is not accounted for in the study. We expect that surface ozone benefits due to forestation would be enlarged when considering reduced ozone levels further lower vegetation damage.”.\n[I guess I was getting at the fact that more ozone at the surface would provide more ozone damage to vegetation, thus limiting the effect you are discussing, not enhancing it. Of course, the limit on the stomatal conductance is yet another order effect that would have the effect you suggest. So, as you say below, greening enhances dry deposition, but doesn’t that mean more of that ozone gets taken up by the vegetation – which would reduce the amount in the atmosphere, but increase the damage done to vegetation.].\nGreening enhances dry deposition, which would then reduce ozone concentrations near the surface and thus vegetation exposure to ozone. Here, to address the impact of greening on ozone-related vegetation damage, we have used the two exposure metrics: GPP reduction (an exposure-based metric) and POD1 (a dose-based metric). We have clarified in Line 149-154 (Page 4-5): “However, the response of ozone-induced vegetation damage to greening effects differs depending on the metric chosen. The average GPP reductions related to ozone exposure in forests over South China decreased by 24% after considering LAI-induced GPP increases of 5% in 2019 compared to 2005, while the dose-based metric of POD1 increased by 10% due to increased stomatal uptake (Supplementary Figs. 5 and 6).”.\nIn the Introduction, we have better introduced the effect of each process in Line 68-73 (Page 2): “Previous studies have demonstrated the complexity of biosphere-atmosphere interactions by accounting for counterfactual scenarios assuming changes in the land type and LAI, e.g., greening can change biogenic volatile organic compound (BVOC, biogenic NMVOC) emissions and hence ozone formation25-27. Changes in the land type and LAI can also alter ozone dry deposition velocity, thus affecting ozone pollution and ozone-induced vegetation damage28-30.”. We now also state in the last paragraph of Introduction, “We use a regional atmospheric chemistry model (WRF-Chem), driven by the satellite-measured LAI and land cover data, coupled with a forest canopy turbulence parameterization and an improved dry deposition scheme to better account for their effects on surface ozone as shown in Fig.1.”\nIn addition, we now define BVOC in Line 70-71 (Page 2): “biogenic volatile organic compound (BVOC, biogenic NMVOC)”.\n[fine]\n\nComment [1-3]: There are 11 experiments as outlined in Supplementary Table 6 (maybe this table should be in the main text as it is kind of crucial). There is a paragraph in the Methods (lines 495-516) explaining these experiments, including text outlining what the differences with the Base represent. There should be another table or column highlighting these effects, as it is difficult going through the results with references to each of the experiments and no clear guidance on what they each represent. Also, the discussion of some of the processes, like future LAI, comes after the Table, so at the time of reading, it doesn’t make a whole lot of sense. There needs to be a clearer explanation of why land cover and NDVI are important for turbulence, LAI for deposition, and LAI and PFT important for emissions (with a note that MEGAN is used for emissions). The future LAI itself essentially comes from bookkeeping methods. What if they used LAI predicted from one of the CMIP6 models related to the two SSP scenarios (SSP126 and SSP370) – how does that, from multi-models, compare to the assumptions they make here about LAI?\nResponse [1-3]: Thanks for pointing it out. We have added a column and notes on each effect in Supplementary Table 6 and moved to the main text as Table 1. Such a large number of experiments is necessary to diagnose each process's effect comprehensively.\nTo explain each process, we now state in the Methods in Line 512-516 (Page 14) in the revised manuscript: “The “canopy” effect of greening is attributable to forest canopy height and land cover changes, as determined by NDVI and land cover data. The “deposition” effect of greening is attributable to changes in dry deposition associated with LAI. The “emission” effect of greening is attributable to changes in BVOC emissions associated with LAI and PFT.”.\n[good changes – thanks]\nOur study statistically predicts future LAI based on the FSV-LAI linear relationship. Unlike the CMIP6 model predictions, our method captures changes in LAI solely due to future forestation, excluding changes due to other factors such as climate change, CO2 fertilization, and nitrogen deposition. We compared our simplified LAI estimates with the high values reported in the literature due to the lack of access to CMIP6 model results for LAI. We now state in Line 225-228 (Page 6-7): “The largest increase in LAI is projected to occur in the evergreen broadleaf forests of southwestern China. The estimated maximum LAI is 7.7 in 2030 and 14.9 in 2050, within the range of historically measured high values40,41.”.\nTable 1 | WRF-Chem model simulations conducted in this study.\nSimulation scenario land cover NDVI LAI in dry deposition LAI in MEGAN PFT in MEGAN Anthropogenic emissions Description\nBase 2019 2019 2019 2019 2019 2017 The present-day condition\nPre-greening 2005 2005 2005 2005 2005 2017 Pre-greening – Base = 2005-2019 greening effects\nCanopy 2005 2005 2019 2019 2019 2017 Canopy – Base = 2005-2019 “canopy” effect1\nDeposition 2019 2019 2005 2019 2019 2017 Deposition – Base = 2005-2019 “deposition” effect2\nEmission 2019 2019 2019 2005 2005 2017 Emission – Base = 2005-2019 “emission” effect3\nLAI2030 2019 2019 2030 2030 2019 2017 LAI-induced greening effects in 2030\nSSP126/ SSP370 2019 2019 2019 2019 2019 2030 SSP126/ 2030 SSP370 The effects of 2030 anthropogenic emission changes\nSSP126LAI2030/ SSP370LAI2030 2019 2019 2030 2030 2019 2030 SSP126/ 2030 SSP370 The effects of 2030 greening and anthropogenic emission changes jointly\nLAI2050 2019 2019 2050 2050 2019 2017 LAI-induced greening effects in 2050\nComment [1-4]: How does Base – pregreening experiments isolate the effect of ozone on vegetation? Between 2005 and 2019 climate is changing, CO2 is increasing, N deposition is presumably increasing – so while greening has increased, and that does have an effect on reducing the ozone, what have they done here to isolate the effect of the ozone? I am unclear in these experiments between 2005 and 2019 how they have accounted for these other factors. I guess the argument is that by accounting for greening they are accounting for these factors, but what about the effect of greening on N deposition, for example (and how that would affect ozone chemistry in the atmosphere) – wouldn’t that counteract the effect of the ozone? It seems to me there should be some experiments with and without the ozone effect itself. So there needs to be an explanation of how greening accounts for that and why no explicit ozone experiments are required.\nResponse [1-4]: Thanks for the comment. Our study aims to assess the impact of greening on surface ozone via atmospheric processes, so that, many other factors, such as climate and CO2 levels, are held constant at 2019 values, excluding their direct effects on ozone. We have now clarified in the Methods, “In all simulations, we hold other factors such as meteorology and CO2 levels constant, allowing us to isolate the effects of greening on ozone.”. As stated in Response 1-2, this study did not include the online ozone-vegetation feedback, and thus, the ozone effect on vegetation is not accounted for.\n[good]\nWe have tested the effects of greening-induced meteorological changes on ozone and discussed in the Supplementary Materials, “We estimate that without the meteorological nudging, the surface ozone reductions caused by forestation greening would be 6% larger from 2005 to 2019 based on the current model settings (Methods; Supplementary Fig. 14)”.\nAs for nitrogen deposition, greening might affect nitrogen deposition by altering the nitrogen cycle and meteorology, which would, in return, impact the greening itself and ozone concentrations. However, this interaction is not included in the WRF-Chem model. We have added the following text in Supplement Information: “Furthermore, changes in PFT and LAI may affect nitrogen deposition by altering the nitrogen cycle and meteorological conditions, which can further impact both the greening trend and ozone concentrations. This process is not incorporated in this study and needs to be evaluated in future work5,7.”.\n[fine]\nReference:\nZ. Zhu et al., Greening of the Earth and its drivers. Nat. Clim. Change 6, 791-795 (2016).\nY. Zhao, L. Zhang, A. P. Tai, Y. Chen, Y. Pan, Responses of surface ozone air quality to anthropogenic nitrogen deposition in the Northern Hemisphere. Atmos. Chem. Phys. 17, 9781-9796 (2017).\n\nComment [1-5]: There are a lot of models and datasets used in this study. It would be useful to have a flow chart diagram that shows how all the different models and datasets are interconnected, and where the improvements are made.\nResponse [1-5]: Thanks for the suggestion. We have now added Figure 1 in the text.\nFig. 1 | The integrated modeling framework for quantifying greening effects on carbon storage and ozone air quality.\n[good]\n\nComment [1-6]: Some of the map figures comparing model to observations (e.g. Fig. S4a, Fig. 2a, b – why doesn’t b have those inset numbers?) I think are showing the observations and using the inset numbers to show how the model compares over the whole of China. If that’s the case, it would be better to show difference maps so the regional differences between model and observations are clearly visible.\nResponse [1-6]: Thanks for the suggestion. We have now added observed monthly AOT40 values and relevant statistics in Figure 2b. Additionally, as per your recommendation, we have revised Supplementary Fig. 3b-c to show the spatial distribution of the MDA8 O3 concentration differences between model simulations and observations.\nFig. 3 | 2005-2019 greening effects on ozone air quality over South China in the growing season. A, B, C, Spatial distributions of MDA8 ozone concentrations (A), monthly vegetation ozone exposure metric of AOT40 (B), and ozone-induced GPP damage (C) over China in 2019 simulated in the WRF-Chem Base model. D, E, F, Changes in surface MDA8 ozone concentrations (D), monthly AOT40 (E), and ozone-induced GPP damage with considering LAI-induced GPP changes (F) due to the 2005-2019 greening effects. Circles in A and B denote measured MDA8 ozone concentrations and monthly AOT40 at the China Ministry of Ecology and Environment (MEE) national network. Mean observed values (OBS), corresponding base simulated results (MOD), and their spatial correlation coefficient (R) are shown as inset.\nSupplementary Fig. 3 | Comparisons between observations and WRF-Chem baseline simulations before and after model modifications in China. a, b, c, Spatial distributions of MDA8 O3 concentrations from MEE (a), simulated MDA8 O3 concentration bias in original WRF-Chem model (b), and simulated MDA8 O3 concentration bias in modified WRF-Chem model (coupling a forest canopy parameterization scheme and modifying the dry deposition scheme and the land surface scheme; c) at 157 stations in South China during 21 August to 6 September 2019. d, e, Diurnal variations of surface O3 concentrations at 7 m (d) and O3 concentration gradients from 30 m and 7 m at Dinghushan (DHS) Forest Ecosystem Research Station from 21 August to 6 September 2019. f, g, Comparisons between surface MDA8 ozone concentrations from MEE observations and WRF-Chem baseline simulations before (f) and after (g) model modifications across China (locations given in the Figure 2A). The scatter-plots using the daily values over China with the linear fitting shown inset. Star in a, b, and c represents the location of the DHS station. Black lines and shading in d and e represent the mean and ±1 standard deviation from stational observations. Blue and red lines in d and e represent corresponding model results before and after modification. Mean observed values (OBS), and corresponding base simulated results (Before, and After), and their spatial correlation coefficient (R), and simulated mean bias (MB), and the index of agreement (IOA) are shown inset.\n[Figure S3 looks same to me as old one except some different numbers for MOD]\n\nComment [1-7]: The overall conclusion is that the effect of the greening in reducing ozone surpasses even potential ozone increases from the emission scenarios (i.e. SSP370) themselves. How much of the improvements in air quality is simply less ozone at the very surface, and more just above the canopy? I supposed that would improve human health, but the ozone is still there – either damaging the vegetation or in the atmosphere, still acting as a greenhouse gas, for example.\nResponse [1-7]: Thanks for the comment. We agree that the \"canopy\" effect results in less ozone at the surface and more ozone above the canopy. However, the net effects of greening (after further accounting for the “deposition” and “emission” effects) are decreases in ozone concentrations both in the lower troposphere and at the surface.\nWe have now added Supplementary Fig.9f and a discussion in Line 204-209 (Page 6) in the revised manuscript: “The presence of the canopy turbulence effect leads to a sharp gradient of ozone near the surface, as recorded by the ozone gradient measurements at DHS and simulated by the model, and reduces the mean surface MDA8 ozone concentrations by 0-12.6 ppbv over South China in April 2019 (Supplementary Figs. 3 and 9). Meanwhile, it increases ozone concentrations above the canopy and redistributes the vertical ozone profile in the lower troposphere.”.\nThe \"deposition\" effect causes ozone to be absorbed by vegetation, thereby damaging it. However, the ozone-induced GPP damage decreases as forest greening through vegetation abundance increases. We have stated in the text, “The average GPP reductions related to ozone exposure in forests over South China decreased by 24% after considering LAI-induced GPP increases of 5% in 2019 compared to 2005, …”, and “Furthermore, future forest greening would decrease ozone-induced GPP damage by 0.5%±2.9% in 2030 and 4.9%±6.7% in 2050 though vegetation abundance increases (Supplementary Fig. 6).”.\nIn addition, the net effect of future greening on ozone is robust under different future emission scenarios, but does not outweigh the impact of anthropogenic emission changes (Fig. 4).\nSupplementary Fig. 9 | Forest canopy turbulence effects on near-surface vertical diffusivity coefficients and ozone concentrations. a, b, Spatial distributions of near-surface vertical diffusivity coefficient changes induced by forest canopy turbulence over China in April 2005 (a) and 2019 (b). c, Differences of averaged vertical ozone concentration profile between before and after coupling a forest canopy parameterization scheme in WRF-Chem over South China in April 2019. d, Modified surface ozone concentrations after considering a forest canopy turbulence scheme in April 2019. e, Differences in 2005-2019 April mean surface ozone concentration changes with vs. without considering the forest canopy turbulence effect. f, Changes in averaged vertical ozone concentration profiles due to the total greening effects. The solid green line in f represents the changes when accounting for the forest canopy turbulence effect, while the dotted green line shows the changes when the forest canopy turbulence effect is not considered. The mean values of changes over South China are shown in a and b.\n[fine]\n\nReviewer #2 (Remarks to the Author):\nComment [2-1]: The paper by Liu and colleagues describes simulations of O3 changes associated with increasing forestation in south China. They describe improvements to the surface turbulence parameterization in the WRF-Chem model and they perform sensitivity tests involving changes in forest area, canopy height, leaf area index over the period 2005-2050. One of their main conclusions is that increasing forest area can decrease near-surface O3 in addition to storing carbon. Since it is well-known to atmospheric chemists that vegetation decreases near-surface O3 via dry deposition (and reaction with BVOCs), the strength of this work is in quantifying both effects together, the co-benefit of forestation.\nResponse [2-1]: Thank you for acknowledging our work that innovatively points out the co-benefit of forestation. We have improved our manuscript based on all your comments and suggestions. In particular, we have improved the description of forest canopy turbulence parameterization to address the raised concerns. Please see our detailed responses below.\n\nComment [2-2]: My main critique of the paper is the way that the turbulence parameterization is implemented in the model and then tested. The turbulence parameterization was previously designed (Maker et al., 2017) to simulate vertical turbulence within the forest canopy of a model with multiple layers of canopy vegetation. The method section of this paper, however, does not mention a multi-layer canopy, so I presume that the authors are using the typical single-layer canopy configuration in which all deposition is assumed to occur at the model surface. In that case, I do not think that applying this in-canopy turbulence parameterization is appropriate.\nResponse [2-2]: Thanks for the comment. Indeed, the treatment of gas dry deposition in this study is based on Wesely’s scheme (Wesely, 2007), where all dry deposition is assumed to occur at the model's surface. This is consistent with the approach used by Marker et al. (2017), in which they also used Wesely’s scheme. Although our study does not consider dry deposition at a multi-layer canopy, we implement the forest canopy turbulence parameterization at the multiple layers of the model (primarily at the first two layers above the ground). We think that the treatment shall adequately address the main focus of this study, despite the caveat raised in Comment [2-3] below. We have added the following text in Methods to improve the description of forest canopy turbulence parameterization, “It has 38 layers vertically from the surface to 50 hPa, with the first and second layers centered at 10 m and 30 m above the ground, respectively. …The forest canopy parameterization is applied to the forest grid cells as defined by the MODIS land cover dataset with the forest fraction exceeding 20%. Due to limitations in model vertical resolution, the parameterization is applied to the lowermost two layers in most forest grid cells.”.\nReference:\nWesely, M. Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmos. Environ. 41, 52-63, doi:10.1016/j.atmosenv.2007.10.058 (2007).\nMakar, P. A. et al. The effects of forest canopy shading and turbulence on boundary layer ozone. Nat. Commun. 8, 15243, doi:10.1038/ncomms15243 (2017).\n\nComment [2-3]: The evaluation of simulated O3 concentration gradients at DHS appears inconsistent with the way observations were collected. At the DHS site, O3 was measured above and within the forest canopy. Thus, part of the O3 gradient is caused by O3 deposition to the upper canopy between the two measurement heights. As mentioned, with the single-layer canopy model, however, all deposition is assumed to occur at the model surface, so that near-surface gradients may be calculated by assuming a constant-flux layer. It thus appears that the model has been induced to match the gradient by excessively reducing the vertical turbulence, rather than by accounting for the vertical distribution of O3 deposition within the canopy. I am thus unpersuaded by all of the conclusions related to how canopy turbulence affects O3 (lines 110-124, lines 197-212, abstract).\nResponse [2-3]: Thanks for the comment. As stated in Response 2-2, the forest canopy turbulence parameterization is applied at the multiple layers of the model, and the WRF-Chem simulation of near-surface concentration gradients accounts for vertical turbulence and surface dry deposition. The difference between observations and simulations is that the observed near-surface concentration gradients include the effect of non-constant flux layer deposition due to canopy structures. We enhanced the model's dry deposition scheme in addition to adjusting the canopy vertical turbulence. These improvements accounted for canopy conditions by incorporating the dependence of canopy stomatal and leaf surface resistances on LAI (Methods). Thus, our results partly take into account the effect of vegetation abundance on dry deposition even though they do not consider the vertical distribution of dry deposition.\nAdditionally, considering the forest canopy turbulence parameterization can improve the simulation of turbulent exchange coefficients and ozone concentrations. We believe that, despite not including ozone dry deposition in the upper canopy in this study, our conclusions regarding the ozone concentrations remain unaffected.\nFor the validation of the vertical canopy turbulence improvement, we have stated in the Methods in Line 456-461 (Page 13) in the revised manuscript: “The model diffusivity coefficient below the canopy reduces to 0.4 m2 s-1 from 4.3 m2 s-1 at the DHS site in August 2019 after coupling the multi-layer forest canopy turbulence parameterization. The adjusted value is slightly lower than the approximately 0.6 m2 s-1 estimated from the concurrent below-canopy ozone eddy covariance flux measurements at the DHS site63. This discrepancy is likely due to the absence of ozone uptake by the upper canopy, leading it to compensate by overly enhancing the suppression of turbulence.”.\nWe have now clarified in Line 112-117 (Page 3): “Recent newly conducted near-surface ozone measurements at the Dinghushan (DHS) Forest Ecosystem Research Station (a forest station in South China; Methods) reveal large ozone vertical gradients from above and below the forest canopy, and we find that capturing the near-surface ozone gradients requires coupling a forest canopy turbulence scheme and improving the dry deposition scheme to the regional air quality model (Methods, Supplementary Fig. 3).”.\n\nComment [2-4]: It is confusing to call sensitivity test 1 “turbulence” since dry deposition changes as well. The authors acknowledge this in the methods, but the rest of the paper is written as though test 1 only change turbulence and not deposition. Response [2-4]: Thanks for pointing it out. We have now revised the “turbulence” effect to “canopy” effect in the text. We have also explained in Line 211-214 (Page 6) in the revised manuscript: “Forest canopy expansion would also induce a modest increase in ozone dry deposition velocity via increases in forestland cover, while we find that such an effect on ozone pollution is much smaller than the suppression of turbulence (Supplementary Figs. 7-8).”.\n\nComment [2-5]: I would like to see a discussion of whether it is possible to double FSV and more than double LAI by 2050 within the constraints of projected forest area and the known species found in these regions. If not, the future scenario is fanciful.\nResponse [2-5]: Thanks for the comment. FSV is expected to increase from 17.6 billion m3 in 2019 to 26.5 billion m3 in 2050 based on the national forestation target. We thus estimate that Forest LAI would increase by 34% in 2050 compared to 2019, according to the linear relationship between FSV and LAI. The estimated LAI increase is less than double in 2050. We now add a discussion of future LAI values in Line 225-228 (Page 6-7): “The largest increase in LAI is projected to occur in the evergreen broadleaf forests of southwestern China. The estimated maximum LAI is 7.7 in 2030 and 14.9 in 2050, within the range of historically measured high values40,41.”.\nWe have now improved the statement in Line 218-221 (Page 6) in the revised manuscript: “Previous forestation goals have been achieved in China's five-year plans. China has now announced an increase in FSV to 19.7 billion m3 by 2030 and to 26.5 billion m3 by 2050, based on which we can estimate future increases in forest LAI and carbon stocks (Methods).”.\nWe have also improved the statement in the Methods section (Future LAI in 2030 and 2050), “China plans to enhance its forestry development in the future through technological advancements and phased forestry goals. We obtain a conservative estimate of the Chinese LAI in 2030 by forestation according to the goal of increasing the national forest stock volume (FSV) by 6 billion m3 in 2030 relative to 2005 (13.7 billion m3) to peak CO2 emissions before 2030 and to achieve carbon neutrality before 2060 (http://english.mee.gov.cn/News_service/media_news/202012/t20201214_812718.shtml, proposed at the Climate Ambition Summit). We also estimate an ambitious LAI based on the goal of expanding FSV to 26.5 billion m3 by 2050 for forestry development proposed at the 19th National Congress (https://bioenergyinternational.com/policy/china-sets-ambitious-forestry-development-goals-2050).”.\nI have several minor comments as well.\nComment [2-6]: L34: “shall” has a legal meaning, which is not applicable here. I suggest “should”\nResponse [2-6]: Thanks for the suggestion. We have revised the word as suggested.\nComment [2-7]: L42: Unclear what “their abundance” refers to.\nResponse [2-7]: Thanks for pointing it out. We have now revised it to “Their density ...”.\nComment [2-8]: L45-46: There are also large areas of deforestation and declining LAI, so the global trend is not uniform, which I think should be reflected here.\nResponse [2-8]: Thanks for the suggestion. We have now rephrased the sentence to “Recent satellite observations have revealed increasing trends in LAI over the globe, reflecting a generally greening Earth since 2000, despite ongoing deforestation in regions such as South America and Africa9,10.”.\nComment [2-9]: L47: “Greening” is not a specific technical term, so it’s not clear what 25% refers to without reading the cited works.\nResponse [2-9]: Thanks for pointing it out. We have now rephrased the sentence to “China accounted for 25% of the global net increase in LAI during the period, mainly driven by forestation”.\nComment [2-10]: L52: Sentence unclear.\nResponse [2-10]: We have now rephrased the sentence to “The recent Intergovernmental Panel on Climate Change (IPCC) report suggests that forestation should remove approximately one-quarter of atmospheric carbon to help limit global warming to 1.5 degrees”.\nComment [2-11]: L458. Are the maximum and minimum canopy heights prescribed as two values for the entire model domain or spatially variable?\nResponse [2-11]: Thanks for the comment. The maximum and minimum canopy heights are prescribed as 35 m and 2 m, respectively, for the entire model domain (as stated in the Methods). We have now improved the statement in the Methods, “where Hcmax and Hcmin are the prescribed maximum and minimum canopy heights and are set to 35 m and 2 m, respectively, for the entire model domain.”, and “The spatial distribution of canopy height is determined by the satellite dataset of NDVI.”.\nComment [2-12]: It’s not clear whether the parameterization of turbulent diffusion within the canopy (p12) is applied only to O3, or also to water vapor, other gases, heat, and momentum. For consistency, all of these should be affected.\nResponse [2-12]: Thanks for pointing it out. We now clarify in Line 431-434 (Page 12) in the revised manuscript: “The parameterization modifies the turbulence diffusivity and then vertical mixing of atmospheric chemical constituents (all gaseous and aerosol species) within the forest canopy, following a localized-near-field approximation proposed by Raupach78”.\nThe improved parameterization is only applied to the chemical species, and physical processes in the model (e.g., heat and momentum diffusion coefficients) are not modified.\nComment [2-13]: What role do crops and agriculture play in the LAI trends. Changes to crop types, increasing crop cycles, and management practices could also contribute to the LAI trends. Fig 1 shows large increases in LAI in regions that do not have increases in dense forest cover (e.g. Yunnan, Hunan, Jiangxi provinces).\nResponse [2-13]: Thanks for the comment. Cropland greening, primarily attributable to human land-use management (such as quick-growing hybrid cultivars, multiple cropping, irrigation, and fertilizer use), also contributes to the net increases in leaf area. In China, 42% of the net increase in LAI comes from forests and 32% from croplands. (Chen et al., 2019). This also explains why forest cover has not increased in some regions, while LAI has increased.\nWe now state in Line 91-93 (Page 3) in the revised manuscript: “Significant increases in LAI have been observed in the past 15 years in South China, with 42% of the net increases attributed to forests and 32% to croplands10.”.\nReference:\nChen, C. et al. China and India lead in greening of the world through land-use 678 management. Nat. Sustain. 2, 122-129, doi:10.1038/s41893-019-0220-7 (2019).\nComment [2-14]: L182 says that the changes follow “normal distributions” but I do not see any evidence of a gaussian functional shape. This should be revised.\nResponse [2-14]: Thanks for pointing it out. We now remove the distribution argument and change the statement in Line 181-183 (Page 5) in the revised manuscript: “The ozone changes due to “deposition” and “emission” effects in South China are less localized compared to the “turbulence” effect and heavily rely on LAI changes.”.\nComment [2-15]: Future increases in forests should be described as a possibility, not a certainty, in the section beginning on line 214.\nResponse [2-15]: Thanks for the comment. We now improve the statement in Line 217-218 (Page 6) in the revised manuscript: “As a potential solution to facilitate carbon neutrality, future forestation could further green South China and may impact both ozone air quality and carbon stocks.”."}, {"round": 2, "reviewer_comments": {"reviewer_1": "My responses are below in square brackets - easier to read due to formatting if I can provide attached file - generally good job of responding to my comments - just a few issues remaining I have highlighted.\n\nComment [1-1]: Ok, it does seem more readable – however, not sure what was moved into the Methods that was not previously there based on the tracked changes.\nResponse [1-1]: Thank you for pointing that out. We moved the specific definitions of the AOT40 and W126 ozone metrics from the main text into the Methods section. Additionally, details on forest canopy turbulence parameterization and the background site comparisons were relocated to the Methods section.\n\nComment [1-2]: I guess I was getting at the fact that more ozone at the surface would provide more ozone damage to vegetation, thus limiting the effect you are discussing, not enhancing it. Of course, the limit on the stomatal conductance is yet another order effect that would have the effect you suggest. So, as you say below, greening enhances dry deposition, but doesn’t that mean more of that ozone gets taken up by the vegetation –which would reduce the amount in the atmosphere, but increase the damage done to vegetation.\nResponse [1-2]: Thank you for your insight! If we use a dose-based metric (e.g., POD1) to assess ozone damage to vegetation, your understanding is absolutely correct. Thus, we have applied a number of vegetation exposure metrics (AOT40, W126, O3RH, and POD1) in this study to account for the impacts of ozone via surface concentration or vegetation uptake.\n\nComment [1-7]: Figure S3 looks same to me as old one except some different numbers for MOD.\nResponse [1-7]: Compared to the old one, Supplementary Fig. 3b-c have been updated from the spatial distributions of simulated MDA8 O₃ concentrations to the spatial distributions of simulated MDA8 O₃ concentration biases relative to measurements.", "reviewer_3": "This paper by Zehui Liu et al. explores the impacts of forestation on air quality and carbon storage in South China. The study is done through sensitivity analyses using revised WRF-Chem. The main conclusion is that forestation reduces ozone concentrations historically, with turbulence mixing being the main contributor. This study has implications to some extent for future nature-based climate solutions from the perspective of atmospheric chemistry. The strength of the study is that many processes are investigated in the model holistically. I have two main critiques. First, the processes presented here are relatively well-known (i.e., strengths and drawbacks) to the modeling community of atmosphere-biosphere interactions, which lessens the novelty of the study. Second, the changes in ozone concentrations seem rather small to me with large standard deviation on a regional scale. One minor editorial suggestion is that the main text seems to me a mix of methods and results. I would make the result session more concise by moving some text to the session of methods. I also find it hard to read at some places because a lot of figures discussed are in the SI.\n\nResponse [3-1]: Thank you for your thoughtful feedback. We have addressed your concerns as follows:\nWe agree that previous studies have examined the complexity of biosphere-atmosphere interactions by considering counterfactual scenarios. However, our study goes further by comprehensively assessing the net effects of actual greening on ozone air quality in South China over the past 15 years. We integrate multiple factors, including emission effects, deposition effects, and the rarely considered canopy effects, to provide a more holistic evaluation of the actual greening impact on air quality. We have now further clarified this in Line 72-81 (Page 2): “Previous studies have demonstrated the complexity of biosphere-atmosphere interactions by accounting for counterfactual scenarios assuming changes in the land type and LAI, e.g., greening can change biogenic volatile organic compound (BVOC, biogenic NMVOC) emissions and hence ozone formation25-27. Changes in the land type and LAI can also alter ozone dry deposition velocity, thus affecting ozone pollution and ozone-induced vegetation damage28-30. A recent study found that forests can affect surface ozone concentrations by changing the vertical diffusivities and turbulent exchanges within and below the canopies31,32. However, this canopy effect is rarely considered in current air quality models, and the net effects of historic actual greening in South China on ozone pollution remain to be determined.”.\nWe acknowledge that the impact of greening on ozone concentrations shows significant spatial variability. The regional average ozone concentration change is about 1.4 ppbv, while local impacts can exceed 8 ppbv. This change in ozone concentration could lead to a 10%-41% change in ozone-induced vegetation damage.\nWe have relocated some of the detailed methodological descriptions and model verification to the Methods section as per your suggestion. We recommend keeping the figures and tables in the supplement as they provide important details not covered in the main text.\n\nI have several minor comments as well.\nComment [3-2]: Line 90: Why April, July, and October? Why not consecutive months?\nResponse [3-2]: Thanks for the comment. Due to limited computational resource, we selected April, July, and October as representative months for the growing season in South China. We have clarified it the Methods section, “We conduct WRF-Chem model simulations for three months (April, July, and October) of 2019 each after a 3-day spin-up for initialization due to limited computing resources. We average values of the three months as the growing season mean.”.\n\nComment [3-3]: Line 117: It seems to me that the model results are evaluated against the observations for the period of August-September 2019 (Figure S3). However, Figure 1 shows the land cover for the months of April, July, and October. Again, in Figure S4, the simulation is for 2016. Can you explain the inconsistency in the months? Is the growing season April-October in the paper?\nResponse [3-3]: Thanks for the comment. In the paper, the growing season is defined as the average of April, July, and October. The simulations for August-September in 2016 and 2019 were explicitly conducted for model improvement and validation purposes, based on the availability of observations during these periods.\nWe have now moved the 2016 background site comparison to the Methods section to avoid confusion.\n\nComment [3-4]: Line 108-130: I’d move this paragraph and the first sentence in the next paragraph to the SI, because the model evaluation seems more like a method to me.\nResponse [3-4]: Thank you for the suggestion. We have moved the majority of model evaluation information to the Methods section (Model improvements for the simulation of land-atmospheric interactions) and removed the methodological descriptions in the next paragraph.\n\nComment [3-5]: Line 146-147: This sentence is confusing to me.\nResponse [3-5]: We have rephrased the sentence to: “The 2005-2019 greening can significantly reduce the exposure of vegetation to ambient ozone.”.\n\nComment [3-6]: Line 146-156: Some metrics (such as AOT40) are shown in Figure 3, and some are in the SI. I’d suggest adding an explanation for the selection of metrics.\nResponse [3-6]: Thanks for the comment. We now state the selection of ozone metrics in the Methods section. “We calculate the maximum daily average 8-h (MDA8) ozone, three exposure-based vegetation ozone metrics (AOT40, W126, and O3RH), and one dose-based vegetation ozone metric of phytotoxic ozone dose (POD1). These metrics are commonly used to quantify ozone air pollution and its impacts on human health and vegetation36,37.”.\n\nComment [3-7]: Table 1: Canopy-Base<0 according to Figure 5, meaning to me that base>canopy and thus ozone concentrations would be smaller with no greening (i.e., 2005 land cover). Is this contrary to what the paper presents?\nResponse [3-7]: Thanks for pointing it out. Figure 5B is consistent with the paper's content that greening reduces surface ozone concentrations over South China, which shows the differences between Pre-greening (2005) and Base (2019). We have now revised the terms in Table 1 to “Base- Pre-greening / Canopy / Deposition / Emission” for clarity."}, "author_response": "Response to the reviewers’ comments\nNature Communications manuscript NCOMMS-24-19589A\nReviewer #1 (Remarks to the Author):\nComment: My responses are below in square brackets - easier to read due to formatting if I can provide attached file - generally good job of responding to my comments - just a few issues remaining I have highlighted.\nResponse: Thank you for your feedback. We are glad to hear that your concerns have been addressed and that you are satisfied with the responses provided. We have provided further responses to your remaining comments below.\nComment [1-1]: Ok, it does seem more readable – however, not sure what was moved into the Methods that was not previously there based on the tracked changes.\nResponse [1-1]: Thank you for pointing that out. We moved the specific definitions of the AOT40 and W126 ozone metrics from the main text into the Methods section. Additionally, details on forest canopy turbulence parameterization and the background site comparisons were relocated to the Methods section.\nComment [1-2]: I guess I was getting at the fact that more ozone at the surface would provide more ozone damage to vegetation, thus limiting the effect you are discussing, not enhancing it. Of course, the limit on the stomatal conductance is yet another order effect that would have the effect you suggest. So, as you say below, greening enhances dry deposition, but doesn’t that mean more of that ozone gets taken up by the vegetation –which would reduce the amount in the atmosphere, but increase the damage done to vegetation.\nResponse [1-2]: Thank you for your insight! If we use a dose-based metric (e.g., POD1) to assess ozone damage to vegetation, your understanding is absolutely correct. Thus, we have applied a number of vegetation exposure metrics (AOT40, W126, O3RH, and POD1) in this study to account for the impacts of ozone via surface concentration or vegetation uptake.\nComment [1-7]: Figure S3 looks same to me as old one except some different numbers for MOD.\nResponse [1-7]: Compared to the old one, Supplementary Fig. 3b-c have been updated from the spatial distributions of simulated MDA8 O₃ concentrations to the spatial distributions of simulated MDA8 O₃ concentration biases relative to measurements.\n\nReviewer #3 (Remarks to the Author):\nComment [3-1]: This paper by Zehui Liu et al. explores the impacts of forestation on air quality and carbon storage in South China. The study is done through sensitivity analyses using revised WRF-Chem. The main conclusion is that forestation reduces ozone concentrations historically, with turbulence mixing being the main contributor. This study has implications to some extent for future nature-based climate solutions from the perspective of atmospheric chemistry. The strength of the study is that many processes are investigated in the model holistically. I have two main critiques. First, the processes presented here are relatively well-known (i.e., strengths and drawbacks) to the modeling community of atmosphere-biosphere interactions, which lessens the novelty of the study. Second, the changes in ozone concentrations seem rather small to me with large standard deviation on a regional scale. One minor editorial suggestion is that the main text seems to me a mix of methods and results. I would make the result session more concise by moving some text to the session of methods. I also find it hard to read at some places because a lot of figures discussed are in the SI.\nResponse [3-1]: Thank you for your thoughtful feedback. We have addressed your concerns as follows:\nWe agree that previous studies have examined the complexity of biosphere-atmosphere interactions by considering counterfactual scenarios. However, our study goes further by comprehensively assessing the net effects of actual greening on ozone air quality in South China over the past 15 years. We integrate multiple factors, including emission effects, deposition effects, and the rarely considered canopy effects, to provide a more holistic evaluation of the actual greening impact on air quality. We have now further clarified this in Line 72-81 (Page 2): “Previous studies have demonstrated the complexity of biosphere-atmosphere interactions by accounting for counterfactual scenarios assuming changes in the land type and LAI, e.g., greening can change biogenic volatile organic compound (BVOC, biogenic NMVOC) emissions and hence ozone formation25-27. Changes in the land type and LAI can also alter ozone dry deposition velocity, thus affecting ozone pollution and ozone-induced vegetation damage28-30. A recent study found that forests can affect surface ozone concentrations by changing the vertical diffusivities and turbulent exchanges within and below the canopies31,32. However, this canopy effect is rarely considered in current air quality models, and the net effects of historic actual greening in South China on ozone pollution remain to be determined.”.\nWe acknowledge that the impact of greening on ozone concentrations shows significant spatial variability. The regional average ozone concentration change is about 1.4 ppbv, while local impacts can exceed 8 ppbv. This change in ozone concentration could lead to a 10%-41% change in ozone-induced vegetation damage.\nWe have relocated some of the detailed methodological descriptions and model verification to the Methods section as per your suggestion. We recommend keeping the figures and tables in the supplement as they provide important details not covered in the main text.\nI have several minor comments as well.\nComment [3-2]: Line 90: Why April, July, and October? Why not consecutive months?\nResponse [3-2]: Thanks for the comment. Due to limited computational resource, we selected April, July, and October as representative months for the growing season in South China. We have clarified it the Methods section, “We conduct WRF-Chem model simulations for three months (April, July, and October) of 2019 each after a 3-day spin-up for initialization due to limited computing resources. We average values of the three months as the growing season mean.”.\nComment [3-3]: Line 117: It seems to me that the model results are evaluated against the observations for the period of August-September 2019 (Figure S3). However, Figure 1 shows the land cover for the months of April, July, and October. Again, in Figure S4, the simulation is for 2016. Can you explain the inconsistency in the months? Is the growing season April-October in the paper?\nResponse [3-3]: Thanks for the comment. In the paper, the growing season is defined as the average of April, July, and October. The simulations for August-September in 2016 and 2019 were explicitly conducted for model improvement and validation purposes, based on the availability of observations during these periods.\nWe have now moved the 2016 background site comparison to the Methods section to avoid confusion.\nComment [3-4]: Line 108-130: I’d move this paragraph and the first sentence in the next paragraph to the SI, because the model evaluation seems more like a method to me.\nResponse [3-4]: Thank you for the suggestion. We have moved the majority of model evaluation information to the Methods section (Model improvements for the simulation of land-atmospheric interactions) and removed the methodological descriptions in the next paragraph.\nComment [3-5]: Line 146-147: This sentence is confusing to me.\nResponse [3-5]: We have rephrased the sentence to: “The 2005-2019 greening can significantly reduce the exposure of vegetation to ambient ozone.”.\nComment [3-6]: Line 146-156: Some metrics (such as AOT40) are shown in Figure 3, and some are in the SI. I’d suggest adding an explanation for the selection of metrics.\nResponse [3-6]: Thanks for the comment. We now state the selection of ozone metrics in the Methods section. “We calculate the maximum daily average 8-h (MDA8) ozone, three exposure-based vegetation ozone metrics (AOT40, W126, and O3RH), and one dose-based vegetation ozone metric of phytotoxic ozone dose (POD1). These metrics are commonly used to quantify ozone air pollution and its impacts on human health and vegetation36,37.”.\nComment [3-7]: Table 1: Canopy-Base<0 according to Figure 5, meaning to me that base>canopy and thus ozone concentrations would be smaller with no greening (i.e., 2005 land cover). Is this contrary to what the paper presents?\nResponse [3-7]: Thanks for pointing it out. Figure 5B is consistent with the paper's content that greening reduces surface ozone concentrations over South China, which shows the differences between Pre-greening (2005) and Base (2019). We have now revised the terms in Table 1 to “Base- Pre-greening / Canopy / Deposition / Emission” for clarity."}]

Decision letter