Vegetation greening drives long-term dust mitigation in Eastern Asia

Domain: neuroscience
Published: 2026-01-27
Editorial outcome: major_revision

Abstract
Dryland vegetation dynamics play a fundamental role in dust mitigation. While weaker winds have driven recent dust decline in Eastern Asia, the long-term influence of vegetation remains overlooked. Here, using a physically based modeling approach that partitions the drivers of dust emissions across time-scales, we reveal that surface wind dominates interannual variability, negatively correlated with the El Niño–Southern Oscillation, Arctic Oscillation, and Pacific Decadal Oscillation. On a multidecadal scale, however, vegetation cover emerges as the key driver, reducing dust emissions by 32.5% since the early 2000s—a trend projected to continue through 2100. Without these vegetation gains, dust emissions would increase under various CMIP6 projections. Vegetation contributions are uneven: greening of perennial dryland vegetation in sparsely vegetated regions (<15% cover; responsible for 95% of dust emissions) offers the greatest mitigation by stabilizing the land surface and suppressing long-term emissions. These findings highlight priority areas for ecological restoration to sustain dust reduction and advance regional sustainability goals.

Introduction
Sand and dust storms (SDS) release up to 2 billion tonnes of dust emissions from surfaces whose susceptibility is modulated by annually, affecting over 150 countries and posing serious health risks, vegetation. Disentangling the relative influence of land surface including excess mortality. These emissions from drylands have processes and atmospheric drivers is therefore critical to under- substantial impacts on climate and public health, and are further standing dust emission variability and informing large-scale land amplified by ecological degradation and land mismanagement. management strategies.

Although both climate variability and land surface changes, particu- Dust emissions are often examined within distinct disciplinary larly changes in vegetation cover, are recognized as major drivers of frameworks: paleoenvironmental and sedimentary studies tend to dust activity across global drylands, surprisingly, their relative emphasizelong-termchangesinsurfaceconditions,suchasvegeta- contributionstodustvariabilityremainunclear.Substantialvegetation tioncoverandsoilerodibility(i.e.,howeasilymineralparticlescanbe changeshavebeenobservedindrylands,buttheireffectsondust pickedupandtransportedbywind),whichareinfluencedbyclimatic activity remain highly heterogeneous because wind merely triggers trends over decades to centuries; while meteorological analyses typically focus on interannual variability, often by analyzing specific years or extreme events driven by surface winds, droughts, and large- scale circulation patterns. This difference between disciplinary perspectives has led to a fragmented understanding of how dust interacts with land and climate across timescales and disciplines. For example, in the Sahel, dust activity rose sharply in the 19th century, coinciding with both drying trends and agricultural expansion. Similarly, historical records from China show increased dust storms during times of population growth and land use intensification under strong monsoon climates. These long-term perspectives can complement recent studies that emphasize the role of meteorological factors, such as wind weakening, in shaping dust trends during 2001–2017, and together highlight the need for a multidisciplinary, scale-dependent understanding of dust–climate–land interactions. These contrasting findings raise a central question: to what extent are dust emission dynamics governed by different drivers across temporal scales?

Despite increasing recognition that dust-generating processes operate across multiple timescales, few studies have explicitly quantified how the relative importance of vegetation and climate drivers varies with frequency. Eastern Asia presents a particularly compelling case study. On the one hand, both afforestation and land stabilization efforts, such as China’s Three-North Shelterbelt Program, and declining surface wind speeds have been associated with reduced dust activity in recent decades. On the other hand, severe dust outbreaks, including those in 2021 and 2023 that blanketed northern China and Mongolia (Supplementary Fig. 1), continue to emerge episodically, raising public concern over the long-term effectiveness of these programs. This apparent contradiction underscores the need to reconcile long-term ecological trajectories with high-frequency climatic variability. However, a comprehensive evaluation of dust drivers remains lacking, both in Eastern Asia and globally.

To disentangle the temporal dynamics of dust variability in Eastern Asia, we developed a timescale-explicit framework to quantify the relative contributions of vegetation and climate drivers (see Supplementary Fig. 2 for an overview). This framework integrates physically based modeling with statistical attribution to assess dust mitigation by vegetation across historical (1982–2023) and future (2015–2100, CMIP6) periods. Dust emissions are analyzed across multiple spatial scales (land uses, ecoregions) to inform targeted mitigation policies. Our study focuses on springtime dust emissions from the Gobi Desert and surrounding regions because (1) it is the primary source of dust affecting densely populated regions in Eastern Asia (Fig. 1a, b, Supplementary Fig. 3a and Methods); (2) dust emissions in this region exhibit strong seasonal variability, with peaks in spring (March–May), dominating annual dust activity (Supplementary Figs. 4 and 5); and (3) dust activity is strongly negatively correlated with vegetation greenness (Supplementary Fig. 4), highlighting the regulatory role of vegetation in dust mitigation.

Results
Climate-driven intra- and interannual dust variability
Dust emissions from Eastern Asia are profoundly influenced by surface wind across the region. As shown by the random-forest algorithm (Fig. 1c), surface wind speed (at a height of 10 m), climatic variables (net solar radiation, air temperature, soil temperature, soil moisture, Aridity Index) and vegetation (green-up date and Normalized Difference Vegetation Index (NDVI)) explained 15%, 8–12%, and 7% of the variance in dust emission, respectively. Similarly, surface wind speed, NDVI, and moisture availability (precipitation or soil moisture) ranked among the strongest drivers of dust emissions in the partial correlation analysis (Fig. 1d, e and Supplementary Fig. 6). The effects of wind speed were particularly pronounced in areas with sparse vegetation and low soil moisture, characterized by erodible surface conditions (Supplementary Fig. 6i).

The climate impacts on dust emissions strongly interact with surface conditions, reflecting spatial heterogeneity in land–atmosphere coupling. The partial least-squares regression model revealed that the variable importance projection (VIP) for key factors is strongly contingent on vegetation biomass and the duration of snow cover (Fig. 1f, g). For example, VIP decreased sharply as vegetation biomass increased when the mean NDVI was > 0.18. By contrast, VIP increased and eventually surpassed VIP when the mean NDVI reached a threshold of 0.3. Both snow-cover duration and spring rainfall contributed regionally to dust variability, with snow duration exerting stronger control than rainfall in high-latitude and alpine regions (Fig. 1g).

Strong surface winds in Eastern Asia are primarily driven by large-scale circulation systems, including the Mongolian Cyclone, the Siberian High, and synoptic-scale low-pressure troughs. We suggest that these patterns are closely linked to broader climate modes, particularly the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) (Supplementary Fig. 7). During La Niña events, surface winds over northern China and Mongolia strengthen, and this linkage may be further amplified during the negative phase of the PDO. We further demonstrate that the PDO can predict dust intensity in Eastern Asia up to six months in advance, even earlier than the Niño 3.4 index (Supplementary Fig. 7a). Additionally, negative phases of the NAO and AO weaken the polar vortex, allowing cold Arctic air to move southward. This strengthens the Siberian High and intensifies the Mongolian Cyclone, leading to more frequent and intense dust storms in the region. These findings underscore the complex interactions between large-scale atmospheric circulation patterns and dust emission in Eastern Asia.

Vegetation contributions to dust emissions (1982–2023)
Our model captures observed variability in dust activity, as indicated by significant correlations with MERRA-2 DOD (R2=0.53–0.67, P<0.001) and with observed dust storm days (R2=0.44–0.55, P<0.001; Supplementary Fig. 8). Simulated dust emissions in the Eastern Sources exhibited a non-monotonic trend—first increasing and then declining over the past four decades (Fig. 2e). Spatially, however, large areas of the Mongolian Plateau continued to experience increasing emissions during 2001–2023 (Fig. 2a, b). To understand the drivers behind these patterns, we identified four primary factors—surface wind speed, vegetation cover, soil moisture, and snow cover—and analyzed their relative contributions using model simulations over the entire study period. This analysis incorporated data from both GIMMS (1982–2015) and MODIS (2001–2023) (Experiment 1, Supplementary Table 1; see Methods). The overlapping interval (2001–2015) represents a high-emission plateau during a transition phase, and is used primarily for cross-validation and consistency in trend attribution, without implying independent or conflicting trends.

Increasing dust emissions from 1982 to around 2000 were primarily driven by enhanced surface wind speeds (+2.1 g m−2 decade−1, P<0.01), with additional contributions from reduced soil moisture in specific regions (+0.2 g m−2 decade−1) (Fig. 2c). During the same period, vegetation changes also contributed positively to dust emissions: browning over the Mongolian grassland (steppe) (~6% of the area, P<0.05) exceeded modest greening in surrounding regions (~3%, P<0.05) (Supplementary Fig. 9b, e), resulting in a small net increase in dust emissions (+0.03 g m−2 decade−1). Together, these factors resulted in a net increase of +2.4 g m−2 decade−1 in dust emissions across the Eastern Sources (Fig. 2a).

In contrast, dust emissions decreased (−4.4 g m−2 decade−1) from 2001 to 2023 (Fig. 2b), mostly due to lower wind speeds and higher vegetation cover (Supplementary Fig. 9). Wind-driven dust emissions decreased across the Eastern Sources by 4.9 g m−2 decade−1, especially for the main sources of dust in northwestern Inner Mongolia (Fig. 2d). Vegetation cover further reduced dust emissions in the same area (−1.5 g m−2 decade−1, P<0.001). By contrast, the drying of topsoil across Mongolia’s dust sources has led to an increase in dust emissions (+2.0 g m−2 decade−1, P<0.05). Snow cover regionally influenced dust emissions, with effects mainly limited to southern Mongolia and the western Tibetan Plateau, where snow cover lasts longer (Supplementary Fig. 10).

The role of vegetation in dust emission was further validated by an additional experiment (Experiment 2, Supplementary Table 1), comparing scenarios with vegetation fixed at its initial-year state versus fully time-varying vegetation cover. Including time-varying vegetation cover improved the model’s ability to reproduce observed dust storm days in downwind regions, with R2 increasing from 0.50 to 0.55 (P<0.001, Supplementary Fig. 8). This finding aligns with Fig. 2e, f, where vegetation-driven reductions in dust emissions became more pronounced after 2000. Specifically, Theil–Sen slopes indicate a 32.5% reduction in dust emissions relative to the fixed-vegetation experiment, consistent with the intensified greening trend in Eastern Sources (Supplementary Fig. 9e). The rolling correlation analysis (Supplementary Fig. 11b) indicates a shift that began in the early 2000s: vegetation became more strongly linked to reductions in DOD, while the influence of wind declined. Separately, vegetation seemed increasingly disconnected from soil moisture and rainfall (Supplementary Fig. 11a), suggesting that other factors—such as CO2 fertilization or ecological restoration—may have played an increasingly significant role.

Vegetation cover increased significantly from 2001 to 2023 across barren land, grassland, and cropland (P<0.001), with mean FVC increases of ~0.3, ~1.4, and ~1.7 per decade, respectively, leading to reduced dust emissions (Supplementary Fig. 12). A substantial area of barren land (7.4–10.7×104 km2; Supplementary Fig. 12a–d) was converted to grasslands over the past three decades. The ongoing greening of desert and steppe ecosystems in the Alashan Plateau, eastern Gobi Desert, and Ordos Plateau have greatly contributed to the decrease in dust emissions (Supplementary Fig. 13a–d). Notably, more than 70% of regionally accumulated dust originates from the Alashan Plateau, which only covers 30% of the Eastern Sources. This region is dominated by xerophytic desert vegetation composed mainly of perennial woody and semi-woody shrubs such as Reaumuria soongarica, Artemisia salsoloides, and Calligonum mongolicum (see Supplementary Table 2 for additional dominant vegetation types). The deep and persistent root systems of these xerophytic shrubs act as ecological stabilizers, reinforcing surface soil structure and mitigating erosion despite their sparse aboveground cover. In contrast, the remaining four ecoregions are dominated by perennial grasses and semi-arid steppe vegetation characteristic of the Mongolian Plateau, and contribute relatively little to dust emissions. Nevertheless, observed water limitations in our study domain (Supplementary Fig. 13e–g), consistent with global patterns, combined with increased grazing pressure, may pose unexpected risks in the absence of adequate management.

Threshold-dependent effects of vegetation on dust mitigation
The path model for the interannual variability in dust activity reveals that wind speed exerts the strongest direct effect (Supplementary Fig. 14a, b). Our model experiments show that surface wind (80.7%) and vegetation cover (20.11%) account for most of the modeled change in dust emissions from 2001 to 2017, based on their isolated contributions relative to 2001 baseline conditions (Fig. 3a–c; Experiment 3; Supplementary Table 1), supporting previous findings. Vegetation also indirectly affects dust emissions through its modification of surface conditions, which in turn alter near-surface wind dynamics and soil-moisture availability (Supplementary Fig. 14c).

Spatial analyses reveal pronounced heterogeneity in the response of dust emissions to vegetation changes (Fig. 3). This pattern arises from a critical vegetation threshold, beyond which bottom-up factors (e.g., vegetation cover and surface roughness) limit the effectiveness of top-down drivers (e.g., wind speed) in generating dust emissions.

Over 95% of dust emissions originate from regions where the spring fractional vegetation cover (FVC), averaged over 2001–2023 from MOD13C1, is below 15% (Fig. 3b). Beyond this threshold, dust emissions become significantly less sensitive to wind speed. The robustness of this threshold arises from interactions among different biomes, plant morphologies, and soil properties (Supplementary Figs. 15–16). Near this threshold, grassland dynamics—such as expansion, contraction, or shrub encroachment—imply potential changes within the sub-threshold range (Supplementary Fig. 17) and play a key role in modulating dust emission responses. Observed increases in grassland area and FVC over bare soil may enhance surface stabilization and suppress dust emissions (Supplementary Figs. 12 and 17c–f). Ecoregions within this sub-threshold zone, including the Alashan Plateau, warrant particular attention, as any reduction in FVC there cannot be offset by improvements elsewhere. Identifying these threshold-dependent effects is crucial for optimizing dust mitigation strategies.

Multidecadal declines in dust emissions in a greener world
Pronounced interannual fluctuations in dust emissions are closely aligned with wind-driven components of simulations under various Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) in CMIP6, particularly in high-frequency patterns (Fig. 4a–c). However, when short-term fluctuations are excluded (dashed boxes in Fig. 4d), vegetation cover shows the strongest correlation with dust emissions over long-term (multidecadal) scales across all scenarios. This shift in dominant drivers is further supported by EEMD-based timescale decomposition (Supplementary Fig. 18), which reveals that wind signals dominate at shorter timescales (e.g., IMF1–IMF3), while perennial vegetation within dust source regions emerges as the leading driver at longer timescales (IMF4 and above). This pattern primarily reflects the influence of perennial, sparsely distributed vegetation within areas of FVC below ~15%, where dust emissions are most sensitive to change.

Dust emissions in future climate scenarios are projected to decrease by the end of the 21st century compared to the period 2015–2035 (Fig. 4 and Supplementary Fig. 19), largely due to increases in vegetation cover (Supplementary Fig. 20). Notably, the greatest reduction occurs under the scenario with the most substantial vegetation greening. Specifically, under SSP2-4.5 and SSP5-8.5, rising vegetation cover is expected to reduce dust emissions by 47% and 64%, respectively (black line in Fig. 4b, c). However, if vegetation remains constant without further greening, dust emissions would stabilize or even increase (purple line in Fig. 4b, c).

As vegetation cover increases, its role in mitigating dust emissions enhances over time (gray bars in Fig. 4a–c). We predict that, despite stable soil-moisture content (Supplementary Fig. 20c), dust emissions will consistently decrease from the late 2050s to the early 2080s (Fig. 4c), given that the positive effect of vegetation cover outweighs the negative effects of increased surface wind speed. Vegetation resilience, particularly of perennial woody and semi-woody shrubs in desert regions (e.g., the Alashan Plateau) and perennial grasses in semi-arid steppe regions (e.g., the Mongolian Grassland), underpins the multi-year mean vegetation status and essential ecosystem services, hence buffering against short-term fluctuations in dust emissions. Regional greening in China’s drylands over past decades has been attributed to favorable climate conditions, CO2 fertilization, and land-use optimization, and is projected to continue within dust source regions under future climate scenarios (Supplementary Fig. 20). Separate projections indicate that China’s dryland extent, defined as regions with an Aridity Index below 0.5, may decline by 9.3%, 13.4%, and 17.3% by 2050, 2070, and 2100, respectively. These trends support our predictions and underscore the potential of gradual desert contraction to mitigate dust emissions.

Implications for dust mitigation and land management policies
Our analysis reveals that dust activity arises not merely from short-term fluctuations, but from long-term trajectories shaped by slowly evolving environmental drivers. Over the past four decades, dust emissions have exhibited a non-monotonic trend, characterized by an initial increase followed by a subsequent decline, driven by the combined influences of vegetation, wind speed, and soil moisture. Ensemble experiments integrating historical observations with CMIP6 scenario simulations indicate that vegetation increasingly mitigates dust emissions over multi-decadal timescales, largely driven by greening of perennial woody and semi-woody vegetation in key dust source regions, including xerophytic deserts such as those in the Alashan Plateau. By contrast, short-term fluctuations in dust emissions are primarily driven by interannual variations in vegetation cover and wind conditions. These results underscore that robust quantification of the long-term influence of vegetation on dust emissions requires extended observations and careful timescale separation; otherwise, the influence of slowly evolving environmental drivers on multidecadal dust trends could be overlooked.

Vegetation interacts with other bottom-up drivers to shape land surface erodibility. Dry topsoil weakens the compactness of soil aggregates, making them more susceptible to erosion. Low soil moisture followed by delayed rainy season can also reduce vegetation activities over time by triggering partial stomatal closure. Meanwhile, reduced surface air humidity can facilitate the emission of finer particulates through the adhesion process. In cold regions, such as the northern Mongolian Plateau, early snowmelt in warm spring can expose the topsoil, offsetting the positive effects of early monsoon and green-up date on dust emissions. These findings inform the complex impacts of surface conditions on dust and their implications for land management.

Feedbacks between vegetation and dust emissions in drylands are shaped by interactions among climate fluctuations, human interventions, and intrinsic vegetation traits. In Mongolian grasslands, persistent severe droughts which are regionally associated with elevated dust emissions (Fig. 2), have constrained vegetation growth, thereby amplifying a positive feedback between vegetation browning and dust generation. However, this negative trend can be mitigated through targeted human interventions, such as China’s Grain for Green program. Initiated around 2000 to restore perennial vegetation in desertification-prone regions of northern China that overlap with parts of the dust sources considered in this study, this program has reduced wind erosion and associated dust emissions. Despite declining water availability, the Eastern Sources as a whole have experienced increasing vegetation greenness, indicating a decoupling between vegetation dynamics and immediate water supply (Supplementary Fig. 11a), likely driven by improved water-use efficiency through reduced stomatal conductance. These findings underscore the importance of considering human interventions, climatic forces, and vegetation physiology together when evaluating dust variability and designing mitigation strategies, particularly for xerophytic herbaceous and shrub communities that stabilize soils against wind erosion.

Socioeconomic changes have dramatically altered the functions and services of dryland ecosystems. Drivers such as wind and solar energy development and rural-urban migration can indirectly affect natural vegetation recovery. Recent rural depopulation in Eastern Sources may enhance vegetation greening (Supplementary Fig. 3), benefiting dust control and potentially becoming a significant future driver. Urban expansion in association with reducing cropland may not always hinder dust control, as impervious surfaces and optimized agricultural practices help minimize the exposure of erodible soil. Consequently, substantial regional greening and associated land cover changes over the past decades largely explains the notable reductions in dust emissions (Supplementary Fig. 12e–g).

Based on our findings, we propose three principles to help reduce dust emissions and their potential impacts in Eastern Asia:
1. Forward-looking principle: Developing a deep understanding of slowly evolving land surface conditions, particularly changes in vegetation cover of dust sources, is essential. The long-term impact of vegetation dynamics is evidenced by our comparison between greening and no-greening scenarios (e.g., Figs. 2e and 4), as well as regional and vegetation-typed differences in FVC effects, suggesting that vegetation greening, whether natural or human-facilitated, increasingly reduces dust emissions over multidecadal timescales.
2. Threshold principle: Identifying both chronic and acute dust sources. In regions with sparse vegetation (below ~15% cover), even small increases in vegetation cover can yield substantial mitigation benefits, whereas beyond this threshold, benefits tend to saturate; thus, intervention costs rise disproportionately as the area of restoration expands. These effects also depend on vegetation type, since the benefits arise not only from coverage but also from other vegetation characteristics. Notably, this threshold serves as an operational guideline rather than a strict restoration target. In areas where vegetation recovery is unfeasible, alternative measures such as soil stabilization and windbreaks can be implemented to achieve similar dust reduction effects.
3. Key-role principle: Prioritizing ecoregions that exert disproportionate influence on dust emissions. For example, the steppes of the Alashan Plateau and the adjacent eastern Gobi Desert have had strong associations with regional dust trends. Our findings further indicate that April—the peak month for spring dust events—contributed most to the reduction in emissions from 2001 to 2017 (Supplementary Fig. 21). This highlights the importance of integrating both spatial and temporal processes when developing effective dust mitigation strategies. These actions are aligned with the forward-looking principle, emphasizing that sustainable development depends on common but differentiated Sustainable Development Goals rather than short-term regional gains.

This study acknowledges several uncertainties and limitations regarding data and model assumptions. First, surface winds, vegetation, and soil moisture are interdependent, and part of the interannual variability in wind or soil moisture may itself be vegetation-driven. Although controlled model experiments were designed to isolate the effects of individual factors, they did not include explicit dynamic coupling among variables. Consequently, feedbacks such as vegetation-induced modification of near-surface winds or soil moisture persistence could not be fully represented, leaving the net vegetation effect uncertain. Second, this study primarily considers the effects of green vegetation, whereas the influence of dry or senescent vegetation—especially critical in regions where strong winds prevail outside the growing season—remains insufficiently constrained. Recent field surveys and satellite-based analyses have demonstrated that shortwave infrared (SWIR) indices, such as the MODIS Soil Tillage Index (STI), can reliably estimate dry vegetation coverage and its control on surface roughness and threshold friction velocity in arid regions. Incorporating such STI-based estimates into future analyses would help quantify the mitigating role of dry vegetation in dust emission and narrow the current uncertainties associated with wind–vegetation interactions. A more detailed discussion of these and other methodological issues is provided in the Supplementary Notes.

Overall, this study provides important support and nuanced insights to guide dust control policies in Eastern Asia. Our analysis helps to clarify the long-term (interdecadal or even longer) influence of vegetation cover on dust variability, which is often masked by atmospheric fluctuations. By offering process-based evidence across multiple timescales, our findings reveal that the benefits of vegetation greening, particularly for perennial dryland vegetation in dust source regions, become increasingly pronounced over longer periods, in conjunction with changes in wind speed. Moreover, conventional metrics that apply uniform vegetation interventions across regions fail to account for the spatially varying contributions of vegetation to dust emissions. We recommend prioritizing efforts to enhance vegetation in areas where cover is below 15%. Additionally, improving our understanding of the interactions between vegetation cover and dust emissions is crucial for enhancing the predictive power of Earth system models. Integrating these findings into policy frameworks will help develop targeted strategies to address both climate change and dust emissions, supporting more sustainable and informed land management practices.

Methods
Datasets of dust emission and determining the Eastern Sources
Monthly dust emission and dust optical depth (DOD), with spatial resolutions of 0.625×0.5°, were obtained from the tavgM_2d_adg_Nx data set in Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) (https://disc.gsfc.nasa.gov/datasets/M2TMNXADG_5.12.4/summary). We used MERRA-2 dust emissions for statistical attribution of driving factors and for comparison with output of another physically-based dust emission model, DuEM v1 (details in later sections). MERRA-2 DOD at 550nm was derived from the variable of “Dust Extinction AOT (aerosol optical thickness)”. Both satellite- and ground-based data for aerosol optical depth were assimilated to improve the distribution of aerosols in MERRA-2. Therefore, we used MERRA-2 DOD to represent the dependent variable in statistical analysis.

Uncertainties in MERRA-2 DOD data stem from the evolving quality and availability of assimilated observational inputs over time. These include AVHRR (1980–2002), AERONET (1999–2014), MISR (2000–2014), MODIS Terra (2000 onward), and MODIS Aqua (2002 onward). For example, MERRA-2 may show discrepancies in regions such as the Taklamakan Desert, where sparse ground observations and regional differences in data assimilation reduce the accuracy of DOD estimates. To address these potential biases, we incorporated independent ground-based observations to validate the dust emission simulations. Specifically, we used springtime (March–May) dust storm day records from five WMO meteorological stations downwind in northern China spanning 2001–2023 (locations shown in Supplementary Fig. 8a; metadata in Supplementary Tables 3 and 4). These records provide an observational benchmark for assessing model performance beyond assimilated datasets, as dust storm day observations from WMO meteorological stations are not assimilated into the MERRA-2 aerosol reanalysis.

As shown in Supplementary Fig. 8c–d, DuEM outputs demonstrate strong agreement with both MERRA-2 DOD (R2=0.67) and WMO meteorological station observations (R2=0.55) after 2001. This dual validation enhances confidence in the reliability of MERRA-2 DOD and confirms its consistency with independent surface-based data. Moreover, although MERRA-2 data prior to 2000 may be less certain due to the limited availability of high-quality observational data for assimilation, the strong consistency between DuEM simulations driven by GIMMS and MODIS vegetation datasets during their overlapping period (2001–2015) (Supplementary Fig. 8c) indicates that the simulated dust emissions are robust to the choice of vegetation input, thereby increasing confidence in the modeled vegetation–dust relationships across the full study period.

Additionally, we employed monthly average dust storm days during 2001–2017 within the Eastern Sources region (35–49°N, 94–126.5°E), derived from Wu et al. This dataset, based on 339 synoptic stations reporting at least one dust storm day, provides an additional proxy for evaluating model performance at the regional scale (Supplementary Fig. 8d).

Given the strong spatial heterogeneity of dust emissions, accurately identifying source regions is essential for quantitative analysis. Based on the correlation between springtime simulated dust emissions and downwind aerosol loading (Fig. 1b), we delineated the Eastern Sources region (35–47°N, 96–122°E; Fig. 1a). Specifically, we calculated the Pearson correlation between spring (March–May) DuEM-simulated emissions at each grid cell and the area-averaged MERRA-2 DOD over the Beijing–Tianjin–Hebei (BTH) region—the most affected downwind area in the North China Plain (red dashed polygon in Fig. 1a). Grid cells showing significantly positive correlations (P<0.05) were classified as part of the Eastern Sources. This method captures the dominant upwind–downwind transport relationships and reflects the underlying physical causality. Notably, the identified region aligns with the prevailing northwesterly winds during spring (Fig. 1a). The resulting Eastern Sources are largely consistent with those defined by Wu et al. However, unlike Wu et al., we excluded the North China Plain from the source region and instead treated it as a receptor area in our analysis, as its dust emissions are minor compared with those from the upwind arid and semi-arid sources.

Caution should be exercised when attributing dust emissions using local DOD measurements or direct image interpretation, as both methods are sensitive to surface reflectance, cloud cover, and threshold settings, which can lead to misidentifying transported dust as local emissions. Our approach connects interannual variations in dust emissions within upwind source regions—considering their driving factors such as climate and surface conditions—with downwind DOD measurements. This strategy enables the detection of potential causal relationships and offers a physically grounded, spatially consistent framework suitable for large-scale analyses.

Climatic and vegetation products
We assembled a comprehensive set of variables based on dataset availability, including climate, vegetation, land use, and topography, to explore their potential impact on dust emissions (Supplementary Table 5). The higher precision of climate and vegetation products from ERA5-Land, AVHRR, and MODIS, compared to other reanalysis or observational data, helps reduce estimation bias caused by the strong spatial heterogeneity of dust emissions. Monthly climatic variables were derived from the ERA5-Land reanalysis dataset at a 0.1×0.1° spatial resolution. Drought severity and duration were assessed using the Aridity Index (ratio between mean annual precipitation and mean annual reference evapo-transpiration) and the self-calibrating Palmer Drought Severity Index (scPDSI). Information for daily snow cover was derived from the AVHRR and MODIS sensors. AVHRR data for snow cover was produced by the Snow project of the European Space Agency (ESA) Climate Change Initiative (CCI), with an original resolution of about 5×5 km. MODIS data for snow cover were derived from MOD10C1 with a resolution of 0.05°. We optimized the two data sets using a daily gap-filling approach to reduce the interference of clouds. Vegetation plays a critical biophysical role in regulating dust emissions. To capture this effect and ensure the robustness of statistical analyses, we used multiple vegetation structure and phenology indicators. Biweekly GIMMS NDVI3g data (1982–2015) were derived from AVHRR at a 1/12° resolution, and Terra MODIS NDVI data (2001–2023) were obtained from monthly MOD13A3 Version 6.1 at a 1 km resolution. PKUGIMMS NDVI data (version 1.2) provide global, spatiotemporally consistent NDVI data at the same spatiotemporal resolution as GIMMS NDVI3g, covering 1982–2022. These data were generated using biome-specific back-propagation neural network (BPNN) models that integrated the GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. To extend the temporal coverage to 2022, PKUGIMMS NDVI was consolidated with MODIS NDVI data (MOD13C1) using a pixel-by-pixel random-forest fusion method. GIMMS LAI4g (1982–2020) is an LAI product that leverages the latest PKUGIMMS NDVI and Landsat LAI samples, correcting for satellite orbital drift and sensor degradation using BPNN models. GLASS LAI (1982–2021) comprises datasets derived from AVHRR and MODIS data, offering long-term temporal coverage, high spatial resolution (1 km and 0.05°), spatial continuity without missing pixels, and validated accuracy through in situ measurements and satellite comparisons.

To assess the potential impact of vegetation seasonality on dust emissions, we derived green-up date (GUD) from the 16-day MODIS NDVI dataset (MOD13C1, 0.05° resolution). The use of biweekly NDVI data, as suggested in previous studies, does not bias the estimation of temporal changes in GUD or temperature sensitivity. We first addressed the deviation in estimating phenology from NDVI time series due to annual variations in background values outside the growing season such as cloud, snow and aerosols (see Shen et al. for specific methods). The Savitzky–Golay filter was used to denoise the NDVI time series. Two threshold methods were then used to define GUD, one based on 20% of the annual NDVI amplitude and the other based on 20% of the relative NDVI amplitude for the entire time, which was calculated as the difference between the robust mean maximum level and the robust mean base level. The outputs from the two methodologies were strongly correlated interannually. We thus used their means for statistical analysis to mitigate uncertainties. For more details and additional variables, please refer to Supplementary Table 5.

Land cover type and ecoregion datasets
Land-use–driven changes in land cover have substantially reshaped global vegetation patterns over recent decades. To assess how land use changes influence vegetation and dust emissions, we used two long-term land cover datasets: MODIS (MCD12Q1, 500 m, Collection 6.1, 2001–2022) and ESA CCI (300 m, 1992–2020). Both datasets were reclassified into four broad biome types—barren, grasslands, croplands, and other—of which only the first three were used due to their dominance in dust source regions. Forests were excluded from the analysis because they are limited to localized mountainous areas surrounding the dust source regions (Supplementary Fig. 12, gray-shaded region).

To inform region-specific dust mitigation strategies, we further examined differences among ecoregions using the ‘Terrestrial Ecoregions of the World’ dataset. This map, maintained by UNEP-WCMC, integrates biodiversity and conservation priorities across global ecoregions. We excluded the Helanshan montane conifer forests, which contributed little to dust emission in Eastern Sources, and selected five other ecoregions for analysis, namely, Alashan Plateau semi-desert, Gobi Lakes Valley desert steppe, Eastern Gobi desert steppe, Mongolian-Manchurian grassland, and Ordos Plateau steppe. Within these ecoregions, we quantified three potential ecological factors influencing vegetation’s role in dust emission. First, Water Limitation Index was assessed using the Pearson correlation between spring scPDSI and NDVI (MOD13A3) during 2001–2023, where positive values indicate drought-constrained vegetation growth (Supplementary Fig. 13e). Second, grazing pressure was represented by the change in total livestock (cattle, sheep, and goats) between 2010 and 2020, based on available FAO statistics, which provide a consistent proxy for grazing intensity across ecoregions. Finally, dominant species and plant functional types in the Alashan Plateau were examined using the 1:1,000,000 Vegetation Map of China, highlighting the importance of perennial woody and semi-woody shrubs in shaping vegetation structure and influencing dust emissions from major source areas (Supplementary Table 2).

Model of dust emission
Quantifying dust emissions is essential for understanding and responding to their variability. Given the complex interplay of multiple factors influencing dust emission, statistical analyses alone face challenges in isolating these effects. Therefore, it is crucial to use numerical models that capture the non-linear impacts of relevant factors on dust particle dynamics in a physical manner. To address this issue, we employed a physically based dust emission model (DuEM v1), developed by Wu et al., which explicitly represents nonlinear responses of dust flux to surface wind speed, vegetation cover, soil moisture, and snow cover. Comparisons with field observations have demonstrated the reliability of the model, which has been extensively applied in regional and global models of dust. A comprehensive description of the model is provided in Supplementary Methods. In this study, we used the DuEM model to simulate dust emission at an hourly resolution over the last four decades and future scenarios.

The spatiotemporal reliabilities of the DuEM model have been verified using records of dust storms, MERRA-2 DOD data and satellite images across hourly to annual scales (Supplementary Figs. 8, 22–23). Compared with MERRA-2 outputs, DuEM dust emissions exhibited a finer spatial resolution of 0.25°×0.25° and demonstrated higher simulation confidence, as interannual correlations with downwind dust storm days increased from R2=0.44 (MERRA-2) to R2=0.55 (DuEM) during 2001–2023 (Supplementary Fig. 23). We found that downwind DOD in regions affected by dust was more effective for model validation than was local DOD in Eastern Sources (Supplementary Fig. 23c). This finding further supports our use of downwind DOD as the dependent variable in our statistical analysis. Incorporating changes to vegetation notably increased the explainability of the frequency of dust storms and identified a more pronounced trend (Supplementary Fig. 8d).

Forcing data for the model of dust emission
The hourly forcing data, including the friction velocity, surface pressure and soil-moisture, were derived from the hourly data of the fifth generation ECMWF atmospheric reanalysis (ERA5) on single levels (surface or near-surface levels). Instantaneous two-dimensional fields were provided hourly at a spatial resolution of 0.25×0.25°. ERA5 began in 1940, but we used the data from 1982–2023. Vegetation cover data are constructed from GIMMS NDVI3g and MODIS NDVI data (MOD13C1) using the improved pixel bipartite model. The two data sets are linearly interpolated to generate daily fields for the dust model (detailed in Supplementary Methods and Supplementary Table 6). We used the two vegetation products to drive the model, which exhibit high consistency for dust emission. In Phase 1, the model was driven by GIMMS vegetation cover data for 1982–2015, while in Phase 2, it was driven by MODIS vegetation cover data for 2001–2023 (Supplementary Fig. 8c). For projections of dust emissions under three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5) outlined in the sixth phase of the Coupled Model Intercomparison Project (CMIP6, last accessed on 31 October 2023), we selected outputs from an ensemble of 12 CMIP6 models to drive our simulations (detailed in Supplementary Methods and Supplementary Table 7 and 8).

Statistical analyses
We re-gridded all relevant metrics to a spatial resolution of 0.05×0.05° before the analysis using cubic resampling. We calculated the spring (March to May) metrics due to the strong correlation between peak dust storms, such as those in Eastern Asia during spring, and annual dust levels. This correlation facilitated the use of spring dust metrics as a proxy for the annual quantities of dust, thus simplifying our statistical analysis. Based on the availability of high-quality data, we used data from the period 2001–2020 to detect and attribute interannual variations in dust emissions. Furthermore, we removed linear trends from the data using MATLAB’s detrend function prior to analysis, enabling a more accurate examination of these fluctuations.

We used the random-forest algorithm to rank these environmental factors based on their influence on the patterns of dust emission (assessed using spring average MERRA-2 dust emissions for 2001–2020). The random-forest algorithm is a nonparametric modeling technique capable of identifying nonlinearities and interactions among independent variables, without making any assumptions about the data. A strength of the algorithm is that we do not need to specify aspects, such as the order of independent variables and their interactions, and the algorithm can identify inherent patterns and effectively avoid overfitting in a very large dataset. We implemented the above analysis using the ‘randomForest’ version 4.7-1.1 R package.

We selected eight key environmental factors based on random forests for conducting a partial correlation analysis. These eight factors can be categorized into wind, moisture (precipitation, snow cover and soil moisture), vegetation (NDVI, LAI and GUD) and temperature; we selected wind speed, soil moisture, NDVI and air temperature as representative controlling factors. We controlled for the representative factors of the other three categories when calculating the partial contribution of a particular factor.

We also investigated the environmental influence on key variables using a partial least-squares regression model (PLSR). In addition to the model coefficients, the PLSR model calculates variable importance (VIP), with its absolute value representing the importance of the factor to the interannual variability of spring dust emissions. The PLSR model has a ‘greater than one’ criterion; a VIP value > 1 demonstrates that the driver has a large influence on dust emission. We also evaluated how the importance of the filtered driving factors varied across environmental gradients, leveraging the strengths of the model, aiming to identify potential thresholds or tipping points imposed by environmental constraints. We implemented the above analysis using the ‘pls’ version 2.8-1 R package.

Wind-driven interannual variations in dust activity may be influenced by large-scale climate patterns through teleconnections. To explore these potential connections, we obtained multiple climate indices from National Oceanic and Atmospheric Administration web pages (NOAA, https://www.cpc.ncep.noaa.gov/ and https://www.ncei.noaa.gov/access/monitoring/products/), including the Antarctic Oscillation (AAO), Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), El Niño-Southern Oscillation (ENSO, using the Niño 3.4 index), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and the Pacific/North American (PNA) pattern, calculated following standard NOAA procedures. Pearson correlation analysis was conducted between spring-time MERRA-2 DOD downwind and these climate indices. We specifically examined these indices for both current and previous year’s months to evaluate their influence on dust activity (Supplementary Fig. 7).

Path analysis
We developed a path analysis to better understand how vegetation affects dust activity based on previous research. Predicted hypotheses were assigned a relative importance within a single-path diagram by decomposing bivariate correlation coefficients into path coefficients. Based on our hypotheses, we built a path model for four indirect effect paths of vegetation on dust emission (Supplementary Fig. 14). Specifically, we examined how increased vegetation cover might reduce surface temperature, thus decreasing soil moisture loss and enhancing soil compaction; directly increase soil moisture, improving soil stability and resistance to wind erosion; enhances surface roughness, which lowers wind speed; and further reduce soil moisture evaporation through decreased wind speed, leading to greater soil compaction and erosion resistance.

We also considered the relative impacts of additional factors, including surface wind speed, temperature, precipitation and spring phenology, on the emission of dust to constrain our model outcomes. We used the ‘piecewise-SEM’ R package to execute our model in R v.4.2.2. To determine the relative effects, we first excluded pixels with P<0.05 (low fitness of the model) and then counted the standardized path coefficients of the remaining pixels. Consistent with the previous statistical analysis, the model was executed using the detrended data from the period 2001–2020 to ensure a focus on interannual fluctuations in dust emissions.

Model experiments
We conducted four controlled experiments to isolate impacts of various factors (Supplementary Table 1).
Baseline: In the first experiment, we established a baseline by considering the variations in all variables, including friction velocity, vegetation cover, soil-moisture content and snow cover. This provided a reference point for understanding the combined effects of these factors on dust emission.
Experiment 1: For this experiment, we focused exclusively on the variations of the target variable and kept the remaining variables fixed at their values from the first year of the data series. Given the seasonal nature of dust emission, we accounted for intra-annual variations in these variables and excluded their interannual changes, as outlined by Wu et al. This helped us understand how specific variables drive dust emissions independently of long-term trends.
Experiment 2: In this experiment, we isolated the effect of vegetation by holding it constant at its values from the first year, while allowing other variables—such as friction velocity, soil moisture, and snow cover—to vary over time. Seasonal variations in vegetation were still included, so friction velocity could respond to seasonal vegetation structure. By comparing the results of this experiment with the baseline, where all factors were allowed to vary, we were able to quantify the specific contribution of vegetation to dust emissions. This approach strengthened the robustness of our results by clearly distinguishing the role of vegetation from other influencing factors.
Experiment 3: When a clear trend in dust emission is present, it becomes possible to quantify the contribution of each driving factor more effectively. We re-evaluated the primary drivers of changes to dust emission for 2001 to 2017 by incorporating refined observations and reanalysis metrics into the DuEM model, following the approach of Wu et al. The contribution of each driver to the variability of dust emission was quantified by isolating its effect between 2010 and 2017 and comparing it to its baseline level in 2001 (Fig. 3). Importantly, we used the term ‘change’ instead of ‘decrease’ because the impact of regional drivers on dust emissions can be either positive or negative.

Detection of dominant drivers of dust emissions at different timescales
To identify the dominant environmental drivers of future dust emissions under different climate scenarios, we applied two complementary approaches: correlation-based statistical analysis and Empirical Ensemble Mode Decomposition (EEMD).
Correlation-based driver attribution: We assessed statistical associations between dust emissions and three drivers—surface wind speed, FVC, and soil moisture—using Pearson and partial correlation analyses. To separate short-term variability from long-term trends, a 20-year Savitzky-Golay filter was applied to the dust time series. We then computed: (1) correlations between each unfiltered driver and unfiltered dust (Pearson-1), and (2) correlations between each driver and the smoothed dust trend (Pearson-2). Partial correlations were also calculated, controlling for the other two variables, to isolate each factor’s unique effect. These analyses were conducted separately for each scenario, identifying drivers of both interannual and multidecadal dust variability (Fig. 4).
Empirical mode decomposition analysis: To further investigate the scale-dependent relationships between dust emissions and their drivers, we applied EEMD using the Rlibeemd package in R (v4.4.1). EEMD decomposes a time series into a finite number of Intrinsic Mode Functions (IMFs), each representing oscillations at distinct timescales.
For each scenario, we applied EEMD to the annual dust emission time series using an ensemble size of 100 and a noise strength of 0.2 (relative to the standard deviation). The resulting IMFs were used to estimate their characteristic timescales based on zero-crossing analysis. We then computed the absolute Pearson correlation coefficients between each IMF and the original (unfiltered) time series of each driver (Wind, FVC, SoilM). This analysis allowed us to track how the influence of each factor evolved with increasing temporal scale (Supplementary Fig. 18).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Vegetation restoration drives long-term dust mitigation in Eastern Asia\n- What are the noteworthy results?\nThe main result of this study is to highlight the respective importance of climate factors and vegetation cover as drivers of\ndust emission affecting northeastern China, over the last decades (back to 1982) and according to climate projections (until\n2100). There is undoubtedly a considerable amount of thorough work behind the manuscript. However, it also raises\nconcerns that are detailed below.\n- Will the work be of significance to the field and related fields? How does it compare to the established literature? If the work\nis not original, please provide relevant references.\nThe motivation of the work should be more clearly explained. As it is, it focuses on an area affected by dust emissions which\nis actually quite a small – yet indeed densely populated - area of northeastern China. Besides, the types of impacts (e.g. on\nhuman health?) are not stated clearly enough to support the motivation of the study.\nAdditionally, there is a clear similarity with the context of the African Sahel, which is located right South of the Sahara, the\nmain dust source worldwide. The Sahel is also very populated and dust impacts the human population who lives downwind\nSaharan dust plumes. It has clear impacts (e.g. connection with meningitis epidemics) in this region, and many studies\nattempted to estimate Saharan and Sahelian dust emissions and their impacts. A parallel with semi-arid Asia could have\nbeen briefly drawn in the introduction or discussion parts of the manuscript.\n- Does the work support the conclusions and claims, or is additional evidence needed?\nAccording to me, there are some discrepancies between the exposed results and the conclusions the authors draw from\nthem:\nWind versus vegetation as drivers of dust emissions: In the abstract (and in Main l. 284-289), the main point of the authors,\nas currently stated in the manuscript, is that wind dominates interannual variability of dust mission, but that the key driver of\ndust emission over multidecadal scale is vegetation. They are preoccupied that recent large dust events would hide the\nlong-term effect of land restoration in the public perception. However, I am not sure that the results support this statement so\nclearly, as can be read in the manuscript itself e.g. in lines 163-168 and line 204. Also, I do not see where in Extended Data\nFig. 4, can be seen the +29.5% of explanatory power of vegetation after 2000 indicated in line 183, especially as ED Fig.4e\n(which seems to compare the baseline experiment with experiment 2) starts after 2000 making it impossible to compare to\nthe pre-2000 period. If this statement refers actually to Fig. 2f, then the gap between yellow and purple curves in year 2000\nshould be better explained.\nl.144-147: I am not sure that Fig. 2 shows what is stated here. As the dust emissions are temporally averaged in panels a\n(1982-2015) and b (2001-2023), I don’t think that it shows a peak in early 2000s for northern China and a subsequent\ndecline. Indeed, the sign of the trends highly depends on the chosen time-period, as we can clearly see from panel e.\nBesides, there is a large gap between Phase 1 and Phase 2 in Fig. 2f, which should be discussed, especially as the\nconsistency between the 2 Phases was supposed to be checked in Extended Data Fig.4c.\nSimilarly, I am not sure that SI Fig. 7 shows what is stated in lines 185-188. How was the inflection point computed? How\nabout the inflection point curve by curve? And if computing 2 inflection points over the period instead of 1? What are the\ndashed lines? That would also be interesting to see annual rainfall in this figure, as vegetation cover is probably highly\nrelated to it.\nVegetation type: The authors focus their analysis on spring (March to May), as it is the time of the year with maximum dust\nemissions in their study area. Extended Data fig. 1 shows that green vegetation in their study area starts growing during this\nperiod (March to May), progressively preventing dust emission. Their analyses consider vegetation indices (NDVI and LAI)\nthat are dedicated to the remote sensing of green vegetation, and consider semi-arid grasslands and croplands, of which\ngreen up is triggered by the annual rainy season. Yet, in spring, the vegetation cover can also be constituted by the\nremaining vegetation of the previous year (i.e. brown vegetation, not green). Thus, I wonder whether brown vegetation\nshould be included in the analysis?\nI also didn’t understand how the vegetation cover was determined for the future scenarios (2020-2100), as the authors need\nto estimate its fractional vegetation cover in order to run their DuEM model to compute dust emissions. This point should be\nclarified, especially as the main point (vegetation cover drives the multidecadal trends) of the article relies heavily on these\nfuture scenarios.\nIn addition, the analysis focuses on vegetation cover, but the cover type is not discussed. For example, 15% cover (a value\nidentified as a threshold by the authors, to prevent wind erosion) does not translate into the same soil protection for aligned\ncereals (potentially inducing corridor effects) or for a scattered herbaceous cover.\nOvergrazing and land degradation: I don’t think that Extended Data Fig7 shows “overgrazing” as stated in line 197,\novergrazing being a quite complex concept that would need to be defined (e.g. by estimating forage production through\nvegetation cover and compare it to the grazing pressure and livestock needs). Same comment on line 300 and line 315; in\naddition, references 42-43-44 on which the authors rely for this statement do not mention overgrazing or not even grazing. I\ndo not really understand how Extended Data Fig.7i supports this statement, as it shows trend of dust emission scattered\nover all grazing pressure differences. Similarly, I think that references 15&16 stress the importance of wind regime in driving\ndust emissions, which seems contradictory with your statement in lines 317-318, that yet cite this references. All this should\nlead to rephrased in a more nuanced way statements in lines 345-347.\nBesides, the figures are very numerous (33 figures, but altogether about 180 panels – some of which are not referred to in\nthe manuscript: main Fig. 2e; extended data Fig. 7 a&d; extended data Fig. 10 a&b; SI Fig. 1a&b; also, SI fig. 3 is cited after\nSI fig. 7; SI Fig. 8&9 are cited after 14, and SI fig. 10 before 8&9). I wonder whether there is perhaps some redundancy\namong the figures, and whether the authors could select the most relevant ones to ease the reading. Most of them are also\nsmall and thus hard to read, even when zoomed.\n- Are there any flaws in the data analysis, interpretation and conclusions?\nThe whole analysis relies on the use of the MERRA dataset, which is use as a reference. However, this dataset itself is not\ndiscussed, in particular the possible impact of its composition (adding more datasets through time) on the temporal trends\nobserved by the authors.\nThe division of observations in two overlapping phases (1982-2015 and 2001-2023) should be clarify. Especially as different\ntrends are attributed to the two phases (l.161, l.169) despite Phase 2 only having 8 years that are not part of Phase 1. If the\ntwo phases show similar data (as suggested by Extended figure 4c) during the years of overlap, the period used for analysis\ncould identify trends of dust emissions with less or no overlapping years.\nThe way the authors determine the borders of what they call “Eastern Sources” is not clear enough. It is shown in Fig. 1b but\nthe way the spatial correlation is computed should be better explained.\nA word on uncertainties in hourly wind speeds would also be interesting, as simulated dust emissions are highly sensitive to\nthis input. Also, what is the temporal resolution of wind speed in future scenarios, and how was it obtained?\nSome references were cited a bit surprisingly to me, as I am not sure it supports the sentence in which they are cited.\nSee above (paragraph on overgrazing); also, ref. 5 line 53 and ref. 11 line 54.\nOther minor comments:\nl. 508 (Methods): why is the comparison to observed dust storm days restricted to 2001-2017?\nl. 517: how do you ensure that PM10 observed in ChinaHighPM10 are dust and not other particles?\nl. 114: “monsoon trough”: I am not sure that’s the proper word.\nl. 145 : The authors state that the dust emissions “peaked in the early 2000s and then declined over the last 40 years” which\nis not possible as this analysis ends in 2023.\nl. 174 : The link between the sentence and extended figure 5 is not clear.\nl. 196-197 : Extended Data Fig. 5b-d does not exist. Should be extended data fig.7.\nFig 1.a: wind speed and direction: are they annual averages?\nl. 207: I don’t quite understand why this would be a paradox.\nl. 210: unclear. Do you mean that land surface properties modulate wind regime?\nl. 261/265: You mention a greening of drylands and desert contraction, however I am not sure that this is a global trend, as\naridification also reaches large parts of the planet. Please specify.\nl.333: this statement would be more convincing if the analysis was restricted to a restored area and compared to an\nunrestored one.\nl.353-354: I am not sure that this has been demonstrated.\nl. 791: cold availability -> code availability\nSupplementary Figure 8d: The display of data with a line between each point is confusing, as there should not be a link\nbetween SSP1-2.6 “After 2050” and SSP2-4.5 “Before 2050” dust emissions. Superimposing SSP could be more relevant.\n- Do these prohibit publication or require revision?\nAccording to me, these concerns need major revisions by the authors.\n- Is the methodology sound? Does the work meet the expected standards in your field?\nAs far as I could assess, the methodology seems thorough. The model for dust emission was already published in a\nprevious paper, and relies on a large range of standard previous works (particularly studies by Y. Shao). Inputs and\nvariables come from state-of-the-art datasets. In the description of the model, the total numbers of variables and of\nparameters of the model could be specified.\n- Is there enough detail provided in the methods for the work to be reproduced?\nI was not able to download the data from : https://doi.org/10.11888/Terre.tpdc.301534", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "Dear Authors,\nI have read your manuscript thoroughly. Based on my understanding of the content, I have provided detailed comments and\nsuggestions throughout the text. I hope that these insights will help you to refine and improve your work.\nPlease find my comments in the attached file.\n[Editorial Note: This attachment has been omitted from this file. For Reviewer #3's comments, please see the rebuttal on\npages 30-35]\nBest wishes,"}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "I acknowledge the work done by the authors to take into account my comments, in particular to demonstrate the robustness\nof the dust emission simulations and of MERRA DOD by comparing them to stations observations. However, I still have\nissues with several aspects of the manuscript.\nI provide below my comments with the associated line numbers from the Revised Manuscript.\nMajor comments:\n1. My main concern deals with the way authors mention “vegetation restoration” all along the manuscript, whereas their\nresults only show the importance of wind and of vegetation cover on spring dust emissions. As also pointed by Reviewer 3,\nthe authors do not perform any comparison of “restored areas” versus “unrestored areas”. Consequently, this is misleading\nfor the reader, giving the impression that vegetation restoration will diminish dust emission in the future over the long-term,\nwhich is not what is demonstrated in the results. The results show that, for the given climate conditions of the 12 CMIP6\nmodels selected by the authors, vegetation greening by 2100 will be large enough, given the concomitant wind intensity, to\ninduce a decreasing trend in the simulated dust emission. Besides, I am not totally convinced by the new perspective of the\nrevised manuscript, focusing on the idea that wind drives short-term dust emissions, while vegetation drives their long-term\ndynamics. I think that it largely depends on the vegetation type, e.g. annual vegetation cover greatly depends on annual\nprecipitation, which can exhibit a large interannual variability, and of wind conditions. This can be les striking for perennial\nvegetation and for woody cover. As the authors specify that 75% of the emitted dust come from the Alashan Plateau, which is\na semi-desert, it would be interesting to know which type of vegetation (annual, perennial, herbaceous, woody…) grows in\nthat area.\nThese misleading mentions can be found in:\nThe title: “Vegetation restoration drives long-term dust mitigation in Eastern Asia”. This needs to be changed as you did not\nperform analysis dedicated to the comparison of “restored” versus “unrestored” areas, accounting for the vegetation type of\nthese areas. The title could be changed for “vegetation cover” but should also be nuanced as the long-term impact of\nvegetation cover on dust emission depends on the vegetation type and on its resilience to potentially unfavorable climate\nconditions.\nl. 40: in the abstract: “Our results demonstrate that vegetation restoration suppresses long-term dust emission”\nl. 54: “vegetation loss”-> I think you mean vegetation cover. “Loss” means that someone or something actually deprives the\nland surface of its vegetation cover. Also l. 65 “vegetation loss”.\nIdem l.56: “recovery”. I think you actually mean “greening” or “re-greening”, which can be due e.g. to climate trends.\nl.59: It is not that much “unexpected” as dust emission largely depends on wind speed.\nl. 65: “land degradation”. This is a complex concept that does not have a consensual and clear definition, neither among\ninternational institutions nor among scientists. How do you define it?\nl. 70: “driven by”. Actually the study by Mulitza mentioned a parallel between an increased dust deposition and human\nactivities. They did not state that humans were a driver of dust emission.\nl.321: this actually depends on vegetation type and the associated interannual variability of the vegetation cover.\nl. 354: as you did not perform any analysis of “restored” areas versus “unrestored” areas, I do not think that you can state that\nthe decorrelation between soil moisture and vegetation cover is related to restoration projects.\nl. 370: “the long term impact of ecological restoration is evidenced by our comparison between greening and no-greening\nscenarios” and l. 373 “the benefits of restoration”: this is very misleading for the reader. As far as I understood, you performed\nsimulations with and without vegetation cover, which is different from simulations accounting or not for vegetation\nrestoration projects. Your results mostly illustrate the importance of the vegetation cover (in the broad sense) for wind erosion.\nl. 401: “long-term effects of vegetation restoration”: there are two misleading elements here. First, vegetation cover can itself\nexhibit a large interannual variability, as does climate and wind speed. This largely depends on the vegetation type (e.g.\nannual or perennial, trees or grass…). Second, your results illustrate the effect of vegetation cover on dust emission, but\nyou did not perform specific analyses comparing “restored” and “unrestored” areas.\n2. My second general comment deals with the number of figures. There are still a huge number of figures/panels (37 panels\nin Main, 67 panels in Extended Data, 104 panels in Supplementary, thus more than 200 panels altogether). My opinion is\nthat this actually overwhelm the reader and makes us loose the central message of this work. Besides, maps figures are\nreally hardly readable. They could be reframed to the study area (90°E-120°E, 30°N-50°N) instead of 70°E-130°E, 25°N-55°N. I think that some of them are redundant or maybe unnecessary. For example, I wonder whether Ext. Data Fig. 3 is\nreally necessary.\nMinor comments:\nl. 114: “ranked as the strongest drivers” -> “ranked among the strongest drivers”?\nl. 115: Fig 1d does not show soil moisture but precipitation.\nL 226: “in associate with higher livestock density” -> “while also experiencing”?\nl. 227: Ext. Fig. 7e-g do not really show a dynamic through time, excepted 7f bottom which show a difference between 2010-\n2015.\nl. 231: what do you mean by “vegetation cover […] governing surface soil moisture”? The link between vegetation and soil\nmoisture in drylands is not straightforward, see e.g. Deng et al 2020 and Gonsamo et al 2021:\nDeng, Y., Wang, S., Bai, X., Luo, G., Wu, L., Chen, F., ... & Lu, Q. (2020). Vegetation greening intensified soil drying in some\nsemi-arid and arid areas of the world. Agricultural and Forest Meteorology, 292, 108103.\nGonsamo, A., Ciais, P., Miralles, D. G., Sitch, S., Dorigo, W., Lombardozzi, D., ... & Cescatti, A. (2021). Greening drylands\ndespite warming consistent with carbon dioxide fertilization effect. Global Change Biology, 27(14), 3336-3349.\nl. 239: Suppl. Fig 9 shows NDVI trends, not absolute values, thus it does not illustrate the “regions with denser vegetation”.\nl. 247: Suppl. Fig. 13 shows land cover types. Thus I am not sure that Suppl. Fig. 13 & 14 illustrate “grassland expansion,\ncontraction, or shrub encroachment”.\nl. 250: “degradation” of what? How do you define it? Do you mean decrease in FCV?\nl. 273-274: this depends on the vegetation cover (lower or greater than 15%)\nl. 283: “expands” has a spatial implication -> Do you mean “increase”?\nl. 288: which type of vegetation? Trees? Bush? Grass? Annual? Perennial? Crops?...\nl. 290: “expansion” means that the spatial extent of vegetation cover increases. Do you mean “vegetation cover increase” (for\na given space)? Besides, this sentence should be moved to introduction or conclusion (or deleted).\nl. 293: this sentence deals with past decades, whereas the section deals with future projections.\nl. 294: “China’s dryland extent”: defined by what? Do these projected numbers account for “restoration projects”?\nl. 349: ref 44 to 46 do not mention “land degradation”.\nl. 351: ref 47 does not mention “restoration”.\nFig. 2e: I still do not quite understand how the authors interpret the trends on the 2001-2015 period: as it cannot be at the\nsame time a decreasing and increasing trend. I see there is a plateau, but then interpreting trends on Phase I and Phase II,\neach of which includes the 2001-2015 period as a large part of their duration, weakens the interpreted trends.\nFig. 5: this is a rather large figure, even larger as it is duplicated into two panels, only differing by a “+” and two “-“.\nRef 13: Gillett -> Gillette\nl. 565: It would be interesting to mention in which years are added critical data (for DOD assessment) in the MERRA2\nalgorithm (e.g. SEVIRI in 2012, OMI in 2004, AMSU in 2002) and to discuss the impacts it may have on the outgoing DOD.\nBesides, you might want to have a look at this recent publication on MERRA skills for DOD:\nZhu, Q., Liu, Y., & Xiao, N. (2025). The Frequency and Intensity of Extreme Dust Events and Related Driving Factors in\nMajor Dust Sources Based on MERRA-2 Aerosol Reanalysis. Journal of Climate.\nl. 573: You mean Ext. Data Fig 5?\nl. 577: Why would MERRA2 DOD be less certain prior to 2001?\nl. 645: Are these monthly time-series?\nl. 660: over which period do you use MODIS data?\nl. 662: are these land cover types annual or perennial? Is there any tree/woody cover in these areas?\nl. 691: Is this the correlation with stations observations?\nl. 711: You mean Ext. Data. Fig. 5?\nl. 747: to the dust -> to the interannual variability of spring dust emissions?\nl. 756-760: Do these products come from the ERA database?\nl. 771: vegetation density -> what do you mean? Vegetation density usually names variables like number of plants per m².\nDo you rather mean vegetation cover (FCV)? Or aerial vegetation biomass (in kg per m²)?...\nl. 798: About the way to take into account vegetation. In experiment 2, the effect of vegetation is held constant, but friction\nvelocity u* varies over time. As u* is actually affected by vegetation cover, how do you deal with this?\nExt. Fig.. 9: In the legend of Fig. 2f, you mention that 2001 exhibits an “anomalously high value”. Thus, I wonder how this\nextremum translates into your conclusion about trends, e.g. in the Ext Fig. 9 that compares 2010-2017 to 2001.\nSupplementary:\nl. 187: emissions -> GHG emissions (to avoid any confusion with dust emission)\nl. 295: 1 g/m² of what?\nWhen mentioning NDVI or FCV (e.g. legend Suppl. Fig. 9 & 10), please specify if it is annual maximum of NDVI and of FCV.\nSuppl. Table 4: It would be interesting to have the period covered by each dataset, along with their temporal resolution.\nPoint-by-point Response to Reviewer 1:\nThe importance of dry vegetation cover should not be underestimated. It is actually crucial in areas where wind erosivity\noccurs out of the green vegetation cycle, which is at least partly the case in your study area, as illustrated by your Extended\nData figure 2. In particular, you do not mention remote sensing indices (like the STI: Soil Tillage Index) that could quite\neasily allow you to assess dry vegetation cover and refine your investigation on its impact on dust emission.\nI do not understand this sentence: “As shown in Extended Data Fig. 5c, dust emissions simulated during Phase 2 (MODIS-\nbased, 2001–2023) exhibit higher correlation with MERRA-2 DOD (R2= 0.67) compared to Phase 1 (GIMMS-based, 1982–\n2015; R2= 0.53), suggesting improved MERRA-2 data quality after 2001 due to better observational constraints.” Do you\nmean that the better agreement of simulations with MERRA proves that MERRA quality is good? That would imply that the\nsimulations are the reference, whereas your article is the other way around: MERRA is taken as the reference, to which the\nsimulations are compared in order to assess their quality.\nCode availability:\nThe code is now accessible and clear, yet the authors might consider adding a “readme” file to explain the structure of the\nfiles and sub-files, and to detail where can be found the different parts of the code of the dust emission model.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Point-by-point response to comments of Reviewer 1\n[Comments] - What are the noteworthy results?\nThe main result of this study is to highlight the respective importance of climate factors and\nvegetation cover as drivers of dust emission affecting northeastern China, over the last decades\n(back to 1982) and according to climate projections (until 2100). There is undoubtedly a\nconsiderable amount of thorough work behind the manuscript. However, it also raises concerns\nthat are detailed below.\n[Responses] We thank the reviewer for acknowledging the scope and effort of our study, as well\nas the relevance of our focus on the respective roles of climate and vegetation in shaping dust\nemissions across historical and CMIP6 projected periods. We appreciate the reviewer’s thoughtful\nconcerns, which we address in detail below. These comments have helped us further clarify the\nanalyses and further strengthen the manuscript.\nThis study provides the first quantitative assessment of how the relative influence of\nvegetation and climate drivers on dust emissions evolves across timescales—a perspective that we\nbelieve is critical for reconciling recent dust trends and informing future land-climate policy.\nSpecifically, 1) we develop a timescale-explicit analytical framework that integrates physically\nbased dust emission modeling with statistical attribution, allowing us to disentangle the effects of\nvegetation and climate drivers over both historical and future periods; 2) using this framework, we\nshow that vegetation-driven reductions in dust emissions become increasingly pronounced over\nlonger timescales; and 3) we identify strong spatial heterogeneity in vegetation effectiveness,\nsuggesting that restoration efforts should prioritize regions with vegetation cover below 15%.\n[Comments] - Will the work be of significance to the field and related fields? How does it\ncompare to the established literature? If the work is not original, please provide relevant\nreferences.\nThe motivation of the work should be more clearly explained. As it is, it focuses on an area\naffected by dust emissions which is actually quite a small – yet indeed densely populated - area of\nnortheastern China. Besides, the types of impacts (e.g. on human health?) are not stated clearly\nenough to support the motivation of the study.\nAdditionally, there is a clear similarity with the context of the African Sahel, which is located\nright South of the Sahara, the main dust source worldwide. The Sahel is also very populated and\ndust impacts the human population who lives downwind Saharan dust plumes. It has clear impacts\n(e.g. connection with meningitis epidemics) in this region, and many studies attempted to estimate\nSaharan and Sahelian dust emissions and their impacts. A parallel with semi-arid Asia could have\nbeen briefly drawn in the introduction or discussion parts of the manuscript.\n[Responses] We have revised the Main Text (Line 49) to better articulate the motivation and\nbroader relevance of our study. The updated text emphasizes that dust storms affect over 150\ncountries and pose serious health risks including excess mortality. We now clarify that\nnortheastern China, though spatially limited, is a densely populated and highly exposed region\ndownwind of the Gobi Desert, making it a policy-relevant case for studying dust–climate–\nvegetation interactions. Furthermore, we draw explicit parallels with other major dust-affected\nregions such as the Sahel, where both ecological degradation and climate variability have\n1\ncontributed to historical dust trends. These revisions aim to situate our study in a broader scientific\nand societal context, and to highlight the relevance of our timescale-explicit framework beyond\nEast Asia.\nLine 49-105: [Sand and dust storms (SDS) release up to 2 billion tonnes of dust annually,\naffecting over 150 countries1 and posing serious health risks, including excess mortality2,3. These\nemissions often originate from drylands, where ecological degradation and land mismanagement\nintensify dust generation, with further consequences for climate and public health4–7. Both climate\nvariability and land surface changes, particularly vegetation loss, are recognized as major drivers\nof dust activity across global drylands5, 8–11. Surprisingly, there has been no clear resolution on\nhow much of the long-term dust decline can be attributed to vegetation recovery versus natural\nclimate variability. Although substantial vegetation changes have been observed in drylands12,\ntheir effects on dust activity remain highly heterogeneous. This is unexpected, given the\nfundamental role of vegetation in surface stabilization and dust suppression13–15. Disentangling the\nrelative influence of land surface processes and atmospheric drivers is therefore critical to\nunderstanding dust variability and evaluating the effectiveness of large-scale restoration efforts.\nDust emissions are often examined within distinct disciplinary frameworks16:\npaleoenvironmental and sedimentary studies tend to emphasize long-term processes such as land\ndegradation and vegetation loss17,18, while meteorological analyses typically focus on interannual\nvariability, often by analyzing specific years or extreme events driven by surface winds, droughts,\nand large-scale circulation patterns8,19,20. This dichotomy has led to a fragmented understanding of\nhow dust interacts with land and climate across timescales and disciplines. For example, in the\nSahel, dust activity rose sharply in the 19th century, driven by both drying trends and agricultural\nexpansion21. Similarly, historical records from China show increased dust storms during times of\npopulation growth and land use intensification under strong monsoon climates22. These long-term\nperspectives complement recent studies that emphasize the role of meteorological factors, such as\nwind weakening23, in shaping dust trends during 2001–2017, and together highlight the need for a\nmulti-timescale understanding of dust–climate–land interactions. These contrasting findings raise\na central question: to what extent are dust dynamics governed by different drivers across temporal\nscales?\nDespite increasing recognition that dust-generating processes operate across multiple\ntimescales, few studies have formally quantified how the relative importance of vegetation and\nclimate drivers varies with frequency. Eastern Asia presents a particularly compelling case study.\nOn the one hand, both afforestation and land stabilization efforts, such as China’s Three-North\nShelterbelt Program, and declining surface wind speeds have been associated with reduced dust\nactivity in recent decades23,24. On the other hand, severe dust events, such as the 2021 and 2023\ndust storms that blanketed northern China and Mongolia (Supplementary Fig. 1)19,25, continue to\nemerge episodically, prompting public concern over the long-term effectiveness of these\nprograms19. This apparent contradiction underscores the need to reconcile long-term ecological\ntrajectories with high-frequency climatic variability. However, a comprehensive, timescale-\nexplicit evaluation of dust drivers remains lacking, both in East Asia and globally.\nTo disentangle the temporal dynamics of dust variability in Eastern Asia, we developed a\ntimescale-explicit framework to quantify the relative contributions of vegetation and climate\ndrivers (Extended Data Fig. 1). This framework integrates physically based modeling with\nstatistical attribution to assess dust mitigation by vegetation across historical (1982–2023) and\n2\nfuture (2015–2100, CMIP6) periods. Dust emissions are analyzed across different spatial (land use,\necoregions) scales to inform targeted policies for dust storm mitigation. Our study focuses on\nspringtime dust emissions from the Gobi Desert and surrounding regions because (1) it is the\nprimary source of dust affecting densely populated regions in Eastern Asia (Fig. 1a, b,\nSupplementary Fig. 2a and Methods); (2) dust emissions in this region exhibit strong seasonal\nvariability, with peaks in spring (March–May), dominating annual dust activity (Extended Data\nFigs. 2 and 3); and (3) dust activity is strongly negatively correlated with vegetation greenness\n(Extended Data Fig. 2), highlighting the role of vegetation in dust mitigation.]\n[Comments] - Does the work support the conclusions and claims, or is additional evidence\nneeded?\nAccording to me, there are some discrepancies between the exposed results and the\nconclusions the authors draw from them:\nWind versus vegetation as drivers of dust emissions: In the abstract (and in Main l. 284-289),\nthe main point of the authors, as currently stated in the manuscript, is that wind dominates\ninterannual variability of dust mission, but that the key driver of dust emission over multidecadal\nscale is vegetation. They are preoccupied that recent large dust events would hide the long-term\neffect of land restoration in the public perception. However, I am not sure that the results support\nthis statement so clearly, as can be read in the manuscript itself e.g. in lines 163-168 and line 204.\n[Responses] We acknowledge the referee’s critical assessment of our conclusions regarding the\nrelative importance of vegetation versus climate. We recognize that our initial framing may have\noverstated vegetation's role. To address this concern directly, we incorporated ensemble empirical\nmode decomposition (EEMD), allowing us to provide a more nuanced, timescale-dependent\nanalysis of vegetation's influence. This analysis (Supplementary Fig. 15) directly supports our\nrevised conclusions regarding the increasing dominance of vegetation at longer timescales. We\nalso strengthened the justification for our conclusions by performing factorial experiments,\nallowing us to isolate the individual contributions of vegetation and climate factors. We have also\nexpanded the discussion of policy implications, drawing parallels with the African Sahel and\nhighlighting how our findings can inform land management strategies aimed at mitigating dust\nemissions in vulnerable dryland regions. We acknowledge the limitations of our analysis,\nincluding our treatment of brown vegetation and the coarse resolution of future projections. We\nhave clarified these limitations in the Supplementary Discussion (Line 238), and we believe they\nhighlight important areas for future research using higher-resolution data and more detailed\nrepresentations of vegetation dynamics.\nIndeed, the attribution of dust trends is complex and timescale-dependent. The two examples\nmentioned—(1) 1982–2015, where vegetation-induced reductions were offset by stronger wind\nand reduced soil moisture, and (2) 2001–2017, where both surface wind decline and vegetation\ngreening contributed to reductions—are both part of our timescale-specific analysis. The second\ncase aligns well with findings from Wu et al. (2022), which also attributed recent dust reduction to\ncombined climate and ecological drivers.\nTo clarify and avoid implying a singular or universal conclusion, we revised the Main Text\n(Line 315) to emphasize the evolving contribution of vegetation over time and the methodological\nbasis for its inferred long-term dominance. Specifically, we incorporate ensemble empirical mode\n3\ndecomposition (EEMD; Supplementary Fig. 15), which is consistent with previous timescale\ndecomposition (Fig. 4d), and shows increasing importance in vegetation cover at longer timescales.\nThe revised text (Line 313-324) reads:\n[Dust activity should be defined based on its long-term trajectory rather than solely on short-\nterm or interannual changes. Over the past four decades, dust emissions have shown no consistent\ntrend due to the combined influences of vegetation, wind speed, and soil moisture. However,\nfactorial model experiments and timescale decomposition reveal that vegetation's contribution to\ndust reduction has steadily increased over longer timescales. This underscores the importance of\nlong-term observational records and appropriate timescale separation to detect vegetation's\ngrowing impact on dust trends. Future projections from CMIP6 further support this view,\ndemonstrating vegetation's dominant role once high-frequency variability is filtered out. Our\nfindings support the hypothesis that the effects of slowly evolving conditions, crucial for long-\nterm projections, may be obscured by the absence of robust process-based quantification and the\nlimited temporal resolution of available data.]\n[\nSupplementary Figure 15 | Correlations between the decomposed dust emissions and major\ndriving signals in the Eastern Sources. a–c, Average driving signals, defined as the absolute\nvalues of Pearson correlations between regional mean drivers and dust emissions. Results are\nshown for CMIP6 scenarios SSP1-2.6 (a), SSP2-4.5 (b), and SSP5-8.5 (c). The x-axis represents\nIntrinsic Mode Functions (IMFs), each capturing variations at distinct timescales, extracted from\nthe dust emission time series using Ensemble Empirical Mode Decomposition (EEMD). The\nupper panels indicate the corresponding timescales (estimated periods) of each IMF.]\nWe hope this revision addresses the concern and better reflects the timescale-dependent\ndynamics revealed by our analysis.\n[Comments] Also, I do not see where in Extended Data Fig. 4, can be seen the +29.5% of\nexplanatory power of vegetation after 2000 indicated in line 183, especially as ED Fig.4e\n(which seems to compare the baseline experiment with experiment 2) starts after 2000 making it\nimpossible to compare to the pre-2000 period. If this statement refers actually to Fig. 2f, then the\ngap between yellow and purple curves in year 2000 should be better explained.\n[Responses] We agree that the original phrasing may have caused confusion. In the revised\nmanuscript (Line 206), we have clarified the relevant period as 2001–2023 (instead of “after\n2000”) to avoid ambiguity. Although we retained the −29.5% value in the revised sentence, we\nhave rephrased it to clearly indicate that it refers to the difference between Experiment 2 and the\nbaseline experiment during the post-2000 period (not a change in explanatory power). Specifically,\nwe now state:\nLine 204-211: [Our analysis indicated that the explanatory power of vegetation cover increased\nduring the 2001–2023 period. This is evidenced by improved model performance when vegetation\nchange was included, with R2 increasing from 0.50 to 0.55 (P < 0.001), based on dust storm day\nobservations in downwind regions (Extended Data Fig. 5d). This finding aligns with Fig. 2f,\nwhere vegetation-driven reductions in dust emissions became more pronounced after 2000 (−29.5%\nrelative to the experiment without vegetation change), consistent with the post-2000 greening\ntrend in Eastern Sources (Supplementary Fig. 4e).]\nWe hope this revised explanation clarifies our findings and their support from multiple lines\nof evidence, including the updated validation using dust storm day records extended to 2023.\n[Comments] l.144-147: I am not sure that Fig. 2 shows what is stated here. As the dust emissions\nare temporally averaged in panels a (1982-2015) and b (2001-2023), I don’t think that it shows a\npeak in early 2000s for northern China and a subsequent decline. Indeed, the sign of the trends\nhighly depends on the chosen time-period, as we can clearly see from panel e. Besides, there is a\nlarge gap between Phase 1 and Phase 2 in Fig. 2f, which should be discussed, especially as the\nconsistency between the 2 Phases was supposed to be checked in Extended Data Fig.4c.\n[Responses] Thank you for pointing this issue. We have revised the figure citation in the main text\n(Line 163) to refer to Fig. 2e rather than Fig. 2a–b when discussing the early-2000s dust peak and\nsubsequent decline over northern China.\nRegarding the apparent gap between Phase 1 and Phase 2 in Fig. 2f, it results from the\ntransition between NDVI datasets—AVHRR (used for 1982–2015) and MODIS (used for 2001–\n2023). This methodological shift may introduce a minor discontinuity in the modeled vegetation\ncontribution, but it does not affect the core finding that vegetation's role has increased over time.\nWe have added this clarification in the Supplementary Discussion (Line 286-300).\n[Although the spatial resolution of products such as MODIS and AVHRR may miss fine-\nscale greening details, the AVHRR dataset is notably affected by issues such as sensor degradation\nand orbital drift, especially around the early 2000s, which can introduce anomalies in the data—\neven though the GIMMS dataset has undergone corrections to mitigate these effects. Nevertheless,\nthe use of these continuous, long-term datasets enables assessment of decadal vegetation trends\nacross broad regions. For example, as shown in Fig. 2f, both GIMMS (analyzed over 2002–2015\nto exclude the anomalously high value in 2001) and MODIS FVC products consistently indicate a\nsignificant increasing trend in vegetation’s contribution to reducing dust emissions, quantified as\napproximately +1 g m−2 per decade (P < 0.05) after 2001. Future research can build upon this\nfoundation by utilizing higher-resolution data (e.g., Landsat, Sentinel) and ground-based\nvalidation to refine attribution of land management effects. Our findings offer a theoretical basis\nfor assessing the effectiveness of vegetation restoration—whether natural or human-driven—in\nmodulating dust emissions across large spatial and temporal scales.]\nIn addition, we would like to clarify the relationship between Fig. 2f and Extended Data Fig.\n4c (now updated as Extended Data Fig. 5c). While the reviewer notes a gap between the two\nhistorical phases in Fig. 2f, this panel represents vegetation-driven dust reduction contributions\nderived from separate experiments using GIMMS (1982–2015) and MODIS (2001–2023) FVC,\nrespectively. In contrast, the original Extended Data Fig. 4c assessed the consistency of dust\nemission trends between GIMMS- and MODIS-driven simulations over their overlapping period\n(2001–2015). This consistency check has now been integrated into the revised Extended Data Fig.\n5c as line plots with shaded ranges and R2 values, rather than the original scatter plot, to better\nillustrate agreement in modeled dust emission trends.\n[\nExtended Data Fig. 5 | Simulated dust emissions during 1982–2023. a and b, Spatial\ndistribution of spring mean dust emissions simulated using vegetation data from GIMMS (a;\nPhase 1: 1982–2015) and MODIS (b; Phase 2: 2001–2023). Purple triangles indicate downwind\ndust storm observation stations. c, Comparison between dust emissions from the two phases and\nMERRA-2 dust optical depth (DOD; black line) at downwind sites. Shaded areas represent the\ntwo simulated phases over the same period (2001–2015), and R2 values indicate their consistency.\nd, Comparison with observed dust storm days. Gray lines show observations from five WMO\nstations located downwind, black line indicates the multi-station mean (2001–2023), and purple\nline shows the average dust storm days across 339 synoptic stations within the Eastern Sources\nregion (2001–2017). Simulated dust emissions correlate with downwind observations (R2 = 0.55)\nand with records from Eastern Sources (R2 = 0.44). The dashed line represents simulated dust\nemissions with vegetation cover fixed at 2001 levels (see Supplementary Table 1). *** indicates P\n< 0.001.]\n[Comments] Similarly, I am not sure that SI Fig. 7 shows what is stated in lines 185-188. How\nwas the inflection point computed? How about the inflection point curve by curve? And if\ncomputing 2 inflection points over the period instead of 1? What are the dashed lines? That would\nalso be interesting to see annual rainfall in this figure, as vegetation cover is probably highly\nrelated to it.\n[Responses] Thank you for your insightful suggestions. We have revised both the main text and\nthe former Supplementary Figure 7 (now updated as Supplementary Figure 8) to remove the\nprevious statement regarding a specific inflection point, as the timing of shifts in the rolling\ncorrelations is not sharply defined curve-by-curve. The updated text (Line 211-217) now states:\n[The rolling correlation analysis (Supplementary Fig. 8b) indicates a shift that began in the\nearly 2000s: vegetation became more strongly linked to reductions in DOD, while the influence of\nwind declined. Concurrently, vegetation seemed increasingly disconnected from soil moisture and\nrainfall (Supplementary Fig. 8a), suggesting that other factors—such as CO₂ fertilization or\necological restoration—may have played an increasingly significant role.]\nAdditionally, revised Supplementary Figure 8 now includes annual precipitation, wind speed,\nsoil moisture, and vegetation trends. This update shows that precipitation has not increased—and\neven declined—since 2000, which aligns with important studies documenting increased drought\nconditions in Inner East Asia and the Mongolian Plateau (Zhang et al., 2020; Hessl et al., 2018).\nMeanwhile, the vegetation cover has continued to increase, indicating a significant role of\necological restoration efforts. Concurrently, the wind speed declined, supporting the dual\ncontribution of increasing vegetation and decreasing wind to the strengthened negative correlation\nbetween vegetation and dust emissions.\n[\nSupplementary Figure 8 | Temporal relationships between vegetation cover and MERRA-2\ndust optical depth (DOD) using 10-year moving windows. a, Standardized trends of major\nenvironmental drivers (spring wind speed, precipitation, soil moisture, and FVC) during 1982–\n2015. All variables were normalized (z-score) to allow comparison on a common scale. Red solid\nlines represent linear trends fitted separately for two phases (1982–2001 and 2001–2015), and\nred dashed lines show the corresponding 95% confidence intervals. b, 10-year moving-window\ncorrelations (starting from 1982–2005) between wind speed, multiple vegetation greenness\nindices, and MERRA-2 DOD in downwind regions. Thin dashed lines show raw correlations, thin\nsolid lines show correlations after detrending the data, and the thick black line shows the mean\ncorrelation across all vegetation indices. Numerical annotations indicate the average correlation in\nthe most recent 10-year window.]\nWe hope these revisions improve the clarity and comprehensiveness and can address your\nconcerns about the interpretation of the correlation analyses.\n[Comments] Vegetation type: The authors focus their analysis on spring (March to May), as it is\nthe time of the year with maximum dust emissions in their study area. Extended Data fig. 1 shows\nthat green vegetation in their study area starts growing during this period (March to May),\nprogressively preventing dust emission. Their analyses consider vegetation indices (NDVI and LAI)\nthat are dedicated to the remote sensing of green vegetation, and consider semi-arid grasslands\nand croplands, of which green up is triggered by the annual rainy season. Yet, in spring, the\nvegetation cover can also be constituted by the remaining vegetation of the previous year (i.e.\nbrown vegetation, not green). Thus, I wonder whether brown vegetation should be included in the\nanalysis?\n[Responses] Thank you for this thoughtful comment. We agree that the senescent or brown\nvegetation—such as litter, crop remains, or other forms of non-photosynthetic vegetation and dead\nbiomass—may also contribute to reducing dust emissions during the spring season by providing\nphysical ground cover. However, the vegetation indices used in this study primarily capture green,\nphotosynthetically active biomass, and therefore do not explicitly reflect brown or non-\nphotosynthetic vegetation components. We have now clarified this limitation in the Supplementary\nDiscussion (Line 278-285), and noted that future studies would benefit from integrating additional\nindicators (e.g., microwave or thermal remote sensing data) to better capture total vegetation\nstructure and surface shielding effects. While our results emphasize the importance of green\nvegetation as a widely used and reliable proxy for dust suppression, we acknowledge that brown\nvegetation could also play a meaningful role and merits further investigation.\n[The vegetation indices used (e.g., FVC and NDVI) primarily reflect photosynthetically\nactive biomass and do not fully capture non-green components such as litter or residues, which\nmay also contribute to surface protection. While this is a limitation, our core findings are\nsupported by process-based modeling: in controlled simulations where vegetation increased, the\ndust emission trend reversed from positive to negative, demonstrating a causal and suppressive\neffect. This provides a theoretical basis for understanding how large-scale vegetation change—\nregardless of origin—can reshape dust emission patterns over time.]\n[Comments] I also didn’t understand how the vegetation cover was determined for the future\nscenarios (2020-2100), as the authors need to estimate its fractional vegetation cover in order to\nrun their DuEM model to compute dust emissions. This point should be clarified, especially as the\nmain point (vegetation cover drives the multidecadal trends) of the article relies heavily on these\nfuture scenarios.\nIn addition, the analysis focuses on vegetation cover, but the cover type is not discussed. For\nexample, 15% cover (a value identified as a threshold by the authors, to prevent wind erosion)\ndoes not translate into the same soil protection for aligned cereals (potentially inducing corridor\neffects) or for a scattered herbaceous cover.\n[Responses] We thank the reviewer for pointing this out. To clarify how future vegetation cover\nwas determined, we have added detailed descriptions in the Supplementary Methods (Line 210)\nand a new table (Supplementary Table 6) listing the CMIP6 variables used in the dust emission\nmodel. Specifically, fractional vegetation cover (FVC) for the future scenarios (2020–2100) was\nestimated using monthly LAI outputs from each CMIP6 model, interpolated to daily resolution via\nlinear interpolation. We then derived daily FVC using the standard exponential function FVC=\n1−𝑒𝑥𝑝(−𝑘×LAI), with 𝑘 = 0.5 (Nilson et al., 1997).\n[Supplementary Table 6 | List of forcing data for projection (2015–2100).\nModel Variable Original Used temporal Reference\nparameter name in temporal resolution\nCMIP6 resolution\nPressure ps Daily Daily\nSoil-moisture mrsos Daily Daily\n(SoilM)\nFriction sfcWind Daily Daily Derived using Eq. (13 and\nvelocity 14)\n(Wind)\nFractional Calculated Monthly Daily Derived using Eq. (15)\nvegetation from “lai”\ncover (FVC)\nSnow cover snw Daily Daily\nfaction\nSoil texture - 250 × 250 m Multiyear mean OpenLandMap\n(http://opengeohub.org/ab\nout-openlandmap/)\n]\nWe appreciate the reviewer’s comment that the same FVC value can correspond to different\nlevels of surface protection depending on vegetation type, structure, or spatial arrangement (e.g.,\naligned crops versus patchy grasses). Due to the limited vegetation-type detail available in CMIP6\nprojections and the scope of this study, we focused on the total green vegetation cover as a\ngeneralized biophysical proxy for dust suppression. We have acknowledged this limitation in the\nrevised Supplementary Discussion (Line 301-307) as a source of uncertainty: [It is important to\nnote that the same fractional vegetation cover (FVC) value can correspond to different levels of\nsoil protection depending on vegetation type, structure, and spatial arrangement (e.g., row crops\nversus patchy grasses). Due to the limited detail on vegetation type in both the historical and\nCMIP6-derived FVC data, our results utilize FVC as a generalized proxy. This introduces a source\nof uncertainty that future research should address by incorporating data on vegetation structure\nand cover classes.].\n[Comments] Overgrazing and land degradation: I don’t think that Extended Data Fig7 shows\n“overgrazing” as stated in line 197, overgrazing being a quite complex concept that would need\nto be defined (e.g. by estimating forage production through vegetation cover and compare it to the\ngrazing pressure and livestock needs). Same comment on line 300 and line 315; in addition,\nreferences 42-43-44 on which the authors rely for this statement do not mention overgrazing or\nnot even grazing. I also do not really understand how Extended Data Fig.7i supports this\nstatement, as it shows trend of dust emission scattered over all grazing pressure differences.\nSimilarly, I think that references 15&16 stress the importance of wind regime in driving\ndust emissions, which seems contradictory with your statement in lines 317-318, that yet cite this\nreferences. All this should lead to rephrased in a more nuanced way statements in lines 345-347.\n[Responses] We fully agree that “overgrazing” is a complex term that requires precise definition\nand supporting evidence, including forage availability and livestock demand, which are beyond\nthe scope of our current analysis. In response:\nWe have revised the original sentence in line 197 to remove the term “overgrazing” and now\nstate (Line 226-228):\n[In contrast, the Gobi Desert has become drier in associate with higher livestock density\n(Extended Data Fig. 7e–g), indicating increased grazing pressure that may contribute to more dust\nemissions.]\nThe original sentence at line 315 has also been revised to remove the term “overgrazing” and\nreflect a more cautious interpretation, now reading (Line 348-350):\n[In Mongolian grasslands, persistent droughts and land degradation have reduced vegetation\ncover44–46, likely contributing to elevated dust emissions (Fig. 2).]\nWith respect to references 15 and 16, we appreciate the reviewer’s point and acknowledge\nthat these studies primarily emphasize wind regime and climate as dominant drivers of recent dust\ndecline. To avoid misinterpretation, we have removed these references from the sentence referring\nto restoration effects and revised it to (Line 351-352):\n[However, this negative trend could be reversed through targeted restoration efforts47,48, such\nas the \"Grain for Green\" project launched in 2000, which aimed to reduce dust emissions in\ndesertification-prone regions (Fig. 5b).]\nWe have revised the relevant discussion to adopt a more nuanced formulation and removed\nreferences to “overgrazing” where not explicitly supported. The revised paragraph (Line 383-\n392) now reads:\n[3. Key-role principle: Prioritizing ecoregions that exert a disproportionate influence on\ndust emissions. For example, the steppes of the Alashan Plateau and the adjacent eastern Gobi\nDesert have had strong associations with regional dust trends. Our findings further indicate that\nApril—the peak month for spring dust events—contributed most significantly to the reduction in\nemissions from 2001 to 2017 (Supplementary Fig. 17). This highlights the importance of\nintegrating both spatial and temporal processes when developing effective dust mitigation\nstrategies. These actions are aligned with the forward-looking principle, emphasizing that\nsustainable development depends on common but differentiated Sustainable Development Goals\nrather than short-term regional gains.56]\nWe believe these revisions more accurately reflect the evidence base, avoid overinterpretation,\nand improve the clarity and nuance of our argument.\n[Comments] Besides, the figures are very numerous (33 figures, but altogether about 180 panels\n– some of which are not referred to in the manuscript: main Fig. 2e; extended data Fig. 7 a&d;\nextended data Fig. 10 a&b; SI Fig. 1a&b; also, SI fig. 3 is cited after SI fig. 7; SI Fig. 8&9 are\ncited after 14, and SI fig. 10 before 8&9). I wonder whether there is perhaps some redundancy\namong the figures, and whether the authors could select the most relevant ones to ease the reading.\nMost of them are also small and thus hard to read, even when zoomed.\n[Responses] When revising, we have corrected the citation order throughout the text and removed\nseveral redundant or unnecessary panels (e.g., the spatial trend maps in Supplementary Fig. 1). To\nimprove clarity we also added an overview diagram (Extended Data Fig. 1) to serve as a\nconceptual framework for our analyses and guide readers through them.\n[\nstations and reanalysis-based dust activity, used to evaluate simulated emissions and their response\nto driving factors. b, Overview of data types used in the study (see Supplementary Tables 2–7 for\ndetails). c, Key drivers of dust emissions across three relevant timescales. d–g, Main results,\nincluding climate teleconnections linked to intra- and interannual variability (d), partial\ncorrelations with local climate and vegetation conditions (e), physically-based dust emission\nsimulations and baseline experiments from 1982 to 2023 (f), and projections under CMIP6\nscenarios from 2015 to 2100 (g). Experimental design is described in Supplementary Table 1.]\n[Comments] - Are there any flaws in the data analysis, interpretation and conclusions?\nThe whole analysis relies on the use of the MERRA dataset, which is use as a reference.\nHowever, this dataset itself is not discussed, in particular the possible impact of its composition\n(adding more datasets through time) on the temporal trends observed by the authors.\n[Responses] We thank the reviewer for pointing this issue. In this revision, we have expanded the\nMethods section to include a dedicated discussion of potential uncertainties in MERRA-2\nassociated with evolving observational inputs over time. We now explicitly address this issue and\nhighlight two key points:\nTemporal reliability: As shown in Extended Data Fig. 5c, dust emissions simulated during\nPhase 2 (MODIS-based, 2001–2023) exhibit higher correlation with MERRA-2 DOD (R2 = 0.67)\ncompared to Phase 1 (GIMMS-based, 1982–2015; R2 = 0.53), suggesting improved MERRA-2\ndata quality after 2001 due to better observational constraints.\nCross-validation: To strengthen confidence in early-period MERRA-2 data, we show that\nthe DuEM simulations driven by GIMMS and MODIS vegetation datasets are highly consistent\nduring their overlap (2001–2015), supporting the reliability of GIMMS-driven simulations in\nPhase 1. In turn, the strong agreement between Phase 1 simulations and MERRA-2 DOD further\nsupports the quality of early MERRA-2 estimates.\nFurthermore, we incorporated independent ground-based observations of spring dust storm\ndays from five NOAA stations in the downwind region (2001–2023; Extended Data Fig. 4d),\nvalidating the model outputs and the robustness of the MERRA-based estimates.\n[\nExtended Data Fig. 5 | Simulated dust emissions during 1982–2023. a and b, Spatial\ndistribution of spring mean dust emissions simulated using vegetation data from GIMMS (a;\nPhase 1: 1982–2015) and MODIS (b; Phase 2: 2001–2023). Purple triangles indicate downwind\n13\ndust storm observation stations. c, Comparison between dust emissions from the two phases and\nMERRA-2 dust optical depth (DOD; black line) at downwind sites. Shaded areas represent the\ntwo simulated phases over the same period (2001–2015), and R2 values indicate their consistency.\nd, Comparison with observed dust storm days. Gray lines show observations from five WMO\nstations located downwind, black line indicates the multi-station mean (2001–2023), and purple\nline shows the average dust storm days across 339 synoptic stations within the Eastern Sources\nregion (2001–2017). Simulated dust emissions correlate with downwind observations (R2 = 0.55)\nand with records from Eastern Sources (R2 = 0.44). The dashed line represents simulated dust\nemissions with vegetation cover fixed at 2001 levels (see Supplementary Table 1). *** indicates P\n< 0.001.]\nThese additions collectively address the concern about potential temporal biases in MERRA-\n2 and reinforce the credibility of our findings.\n[Comments] The division of observations in two overlapping phases (1982-2015 and 2001-2023)\nshould be clarify. Especially as different trends are attributed to the two phases (l.161, l.169)\ndespite Phase 2 only having 8 years that are not part of Phase 1. If the two phases show similar\ndata (as suggested by Extended figure 4c) during the years of overlap, the period used for analysis\ncould identify trends of dust emissions with less or no overlapping years.\n[Responses] The two phases (1982–2015 and 2001–2023) correspond to the use of different\nsatellite vegetation datasets (GIMMS and MODIS, respectively) that were used to drive the DuEM\nmodel. The overlap (2001–2015) is necessary to ensure comparability and consistency across\nsimulations using different vegetation inputs.\nTo address the reviewer’s concern regarding the trend attribution, we clarified in the revised\nmanuscript that our key conclusion focuses on the non-monotonic trend of dust emissions over the\nentire 1982–2023 period, rather than interpreting trends independently within each phase.\nSpecifically, we now describe the results as:\nLine 158-160: [Simulated dust emissions in the Eastern Sources exhibited a non-monotonic\ntrend—first increasing and then declining over the past four decades (Fig. 2e).]\nLine 162-169: [To understand the drivers behind these patterns, we identified four primary\nfactors—surface wind speed, vegetation cover, soil moisture, and snow cover—and analyzed their\nrelative contributions using model simulations over the entire study period. This analysis\nincorporated data from both GIMMS (1982–2015) and MODIS (2001–2023) (Experiment 1,\nSupplementary Table 1; see Methods). The two overlapping phases, which both encompass the\npeak emission period, enable cross-validation and trend attribution, without implying independent\nor conflicting trends.]\nAdditionally, we provide a direct comparison of linear trends using GIMMS-driven\nsimulations for both 1982–2001 and the full Phase 1 (1982–2015) in Fig. 2e. This comparison\nshows that the increasing trend over 1982–2015 remains statistically significant (P < 0.01), and in\nfact appears to be stronger than when truncated at 2001. This suggests that including the\noverlapping years (2001–2015) improves the statistical robustness of the trend analysis rather than\nweakening it.\nMoreover, since both GIMMS- and MODIS-driven simulations exhibit consistent dust\nemission magnitudes during their overlap (2001–2015) (Extended Data Fig. 5c), and both fall\n14\nwithin the high-dust-emission period, we believe it is appropriate to preserve the overlap in order\nto anchor both simulations and maintain continuity across the full time series. Thus, rather than\nexclude the overlapping period, we retained it to enhance the robustness of long-term trend\ninterpretation.\n[Comments] The way the authors determine the borders of what they call “Eastern Sources” is\nnot clear enough. It is shown in Fig. 1b but the way the spatial correlation is computed should be\nbetter explained.\n[Responses] We appreciate the reviewer’s comment regarding to the geographical definition of the\n“Eastern Sources” region. To improve clarity, we have also revised the Methods section to\nexplicitly describe our approach (Line 588). Specifically, we calculated the Pearson correlation\nbetween spring (March–May) DuEM-simulated dust emissions at each grid cell and the area-\naveraged MERRA-2 DOD over the Beijing–Tianjin–Hebei (BTH) region, which we identified as\nthe most impacted downwind area in the North China Plain (Fig. 1a). Grid cells with significantly\npositive correlations (P < 0.05) were defined as part of the Eastern Sources.\nThis definition is broadly consistent with that used in Wu et al. (Ref. 23), but with one key\nmodification: we excluded the North China Plain itself from the source region and treated it as a\nreceptor to better capture upwind-downwind causal relationships. The identified region also shares\na coherent climatological pattern of springtime prevailing northwesterly winds (as shown in Fig.\n1a), further supporting its physical consistency as a dust source zone influencing BTH. These\nclarifications have been incorporated into the revised manuscript (Methods, Line 585-599). [Given\nthe strong spatial heterogeneity of dust emissions, accurately identifying source regions is\nessential for quantitative analysis. Based on the correlation between springtime simulated dust\nemissions and downwind aerosol loading (Fig. 1b), we delineated the Eastern Sources region (35–\n47°N, 96–122°E; Fig. 1a). Specifically, we calculated the Pearson correlation between spring\n(March–May) DuEM-simulated emissions at each grid cell and the area-averaged MERRA-2\nDOD over the Beijing–Tianjin–Hebei (BTH) region—the most affected downwind area in the\nNorth China Plain (red dashed polygon in Fig. 1a). Grid cells showing significantly positive\ncorrelations (P < 0.05) were classified as part of the Eastern Sources. This method captures the\ndominant upwind–downwind transport relationships and reflects the underlying physical\ncausality59. Notably, the identified region aligns with the prevailing northwesterly winds during\nspring (Fig. 1a). The resulting Eastern Sources are largely consistent with those defined by Wu et\nal. 23. However, unlike Wu et al.23, we excluded the North China Plain itself from the source region\nand instead treated it as a receptor area for evaluating dust transported from the Eastern Sources.]\n[Comments] A word on uncertainties in hourly wind speeds would also be interesting, as\nsimulated dust emissions are highly sensitive to this input. Also, what is the temporal resolution of\nwind speed in future scenarios, and how was it obtained?\n[Responses] We appreciate the reviewer’s suggestion to clarify the temporal resolution and\ntreatment of wind speed inputs, given their critical role in driving dust emission simulations.\nIn our historical simulations (1982–2023), we used hourly friction velocity (𝑢 ) directly from\n∗\nERA5 reanalysis data (Supplementary Table 5), which provides high temporal resolution and is\n15\nwidely validated. This allowed for more accurate representation of wind variability relevant to\nthreshold-driven dust emission processes.\nFor future scenarios, however, friction velocity is not available in CMIP6 model outputs. To\naddress this limitation, we estimated 𝑢 from daily near-surface wind speed using the logarithmic\n∗\nwind profile, combined with an empirical relationship for aerodynamic roughness length based on\nfractional vegetation cover (FVC). While the use of daily wind speed may introduce uncertainty—\nparticularly in capturing sub-daily peak wind events—our dust emission simulations still capture\nstrong interannual variability, consistent with the year-to-year fluctuations in CMIP6 wind forcing.\nThis indicates that daily wind inputs are sufficient to resolve key temporal dynamics relevant to\ndust activity at the seasonal to interannual scale.\nRelevant details and associated uncertainties have been added to the Supplementary Methods\n(Line 198) and Supplementary Discussion (Line 308-319) for clarity and completeness.\n[In future projections driven by CMIP6 scenarios, friction velocity (𝑢 ) is not directly\n∗\navailable. Therefore, we estimated 𝑢 from near-surface wind speed (𝑢 ) using the\n∗ 𝑧\nlogarithmic wind profile equation:\n𝑢 =\n𝑘 × 𝑢𝑧\n(13)\n∗ 𝑧\nln( )\n𝑧0\nWhere 𝑘 = 0.4 is the von Kármán constant, 𝑧 is the standard measurement height (10\nm), and 𝑧 is the aerodynamic roughness length. The roughness length was estimated\n0\nas a function of fractional vegetation cover (FVC) using an empirical formulation14:\n𝑧 = 0.0002×FVC+0.004 (14)\nsparsely vegetated soil, while the FVC-dependent term reflects the influence of\nvegetation on aerodynamic drag.]\n[In addition, uncertainties in wind speed inputs may also influence the simulated\ndust emissions. For the historical period, we used hourly friction velocity directly\nfrom ERA5, which offers high temporal resolution and captures short-term wind gusts\nrelevant to dust uplift. In contrast, future projections are based on daily mean near-\nsurface wind speeds from CMIP6 models, which may underestimate peak winds that\ndrive threshold-based dust emissions. Although we applied a physically grounded\nconversion to friction velocity and scaled fluxes accordingly, this lower temporal\nresolution represents a structural limitation in future scenario simulations.\nNevertheless, our simulations successfully captured the interannual variability in dust\nemissions driven by CMIP6 wind inputs, supporting the robustness of the approach.\nImproved representations of wind variability in climate projections would help reduce\nthis uncertainty.]\n[Comments] Some references were cited a bit surprisingly to me, as I am not sure it supports the\nsentence in which they are cited. See above (paragraph on overgrazing); also, ref. 5 line 53 and\nref. 11 line 54.\n[Responses] As previously addressed in our response regarding the use of the term “overgrazing,”\nwe have revised the associated sentences to remove or rephrase this term and provide a more\n16\ncautious interpretation based on available evidence.\nIn addition, we agree with the reviewer that references 5 and 11 were not well aligned with\nthe surrounding statements in the original version. These references have now been removed to\navoid misinterpretation, and we have carefully reviewed all remaining citations to ensure\nappropriate support for each claim.\nOther minor comments:\n[Comments] l. 508 (Methods): why is the comparison to observed dust storm days restricted to\n2001-2017?\n[Responses] The original 2001–2017 dust storm day dataset from Wu et al. (2022) over the\nEastern Sources region is now used as supplementary validation. To strengthen our evaluation, we\nhave added a longer time series (2001–2023) of springtime dust storm day records from five\nNOAA-operated downwind stations across northeastern China, which are now described as part of\nthe main validation dataset (Supplementary Table 2). These independent surface-based\nobservations complement the MERRA-2 DOD (1982–2023) data and support the robustness of\nour DuEM simulations over both short and long timescales.\nThis clarification has been incorporated into the revised Methods section (Line 565-584).\n[Considering potential uncertainties in MERRA-2 data associated with evolving\nobservational inputs over time, we incorporated independent ground-based observations to\nvalidate the dust emission simulations. Specifically, we used springtime (March–May) dust storm\nday records from five WMO meteorological stations downwind in northern China spanning 2001–\n2023 (locations shown in Extended Data Fig. 5a; metadata in Supplementary Tables 2 and 3).\nThese records provide an observational benchmark for assessing model performance beyond\nassimilated datasets.\nAs shown in Extended Data Fig. 4c–d, DuEM outputs demonstrate strong agreement with\nboth MERRA-2 DOD (R2 = 0.67) and WMO meteorological station observations (R2 = 0.55)\nafter 2001. This dual validation enhances confidence in the reliability of MERRA-2 DOD and\nconfirms its consistency with independent surface-based data. Moreover, although MERRA-2 data\nprior to 2001 may be less certain, the high consistency observed between DuEM simulations using\nGIMMS and MODIS vegetation datasets during their overlapping period (2001–2015) (Extended\nData Fig. 5c) supports the credibility of the entire simulation period, including earlier years.\nAdditionally, we employed monthly average dust storm days during 2001–2017\nwithin the Eastern Sources region (35–49°N, 94–126.5°E), derived from Wu et al.23.\nThis dataset, based on 339 synoptic stations reporting at least one dust storm day,\nprovides an additional proxy for evaluating model performance at the regional scale.]\n[Comments] l. 517: how do you ensure that PM10 observed in ChinaHighPM10 are dust and not\nother particles?\n[Responses] To avoid ambiguity regarding particle composition, we have removed the\nChinaHighPM10 dataset from the analysis and no longer refer to it in the revised manuscript. This\ndataset was initially considered as a supplementary line of evidence to illustrate the recent\n17\nresurgence of spring dust activity after 2018, but it was not essential to our core findings.\nTo more robustly capture this trend, we now rely on DOD (dust optical depth) trends over\nboth the Eastern Sources and downwind BTH region, as well as newly added observed dust storm\nday records from five WMO stations (2001–2023), presented in Supplementary Fig. 1. Although\nSupplementary Fig. 1 partially overlaps with Extended Data Fig. 5, it provides additional insights\nby displaying the interannual standard deviation of DOD in both regions. The lower variability in\nthe downwind DOD further supports its reliability in reflecting interannual dust emission changes\nfrom the source area, and serves as an important line of validation for our model output.\n[\nSupplementary Figure 1 | Temporal trends of dust activity in Eastern Asia from 1982 to 2023.\nTime series of MERRA-2 dust optical depth (DOD) in Eastern source regions (thin line) and\ndownwind affected regions (thick lines), with shaded areas indicating uncertainty ranges. Yellow\nbars represent the average number of springtime dust storm days recorded at five meteorological\nstations in the downwind regions. The yellow shading marks the post-2018 period, during which\ndust activity showed short-term increases following a prior declining trend (2001–2017).]\n[Comments] l. 114: “monsoon trough”: I am not sure that’s the proper word.\n[Responses] We thank the reviewer for raising this point. While some previous studies (e.g., Shao\net al., 2013) have included “monsoon troughs” among the synoptic-scale systems responsible for\ndust storm initiation, we agree that the term may introduce ambiguity, particularly in the East\nAsian mid-latitude context.\nTo improve clarity and accuracy, we have revised the sentence (Line 130) to refer instead to\n[synoptic-scale low-pressure troughs8,23] which better reflects the seasonal circulation patterns\nrelevant to dust-generating winds in our study domain.\n[Comments] l. 145: The authors state that the dust emissions “peaked in the early 2000s and then\ndeclined over the last 40 years” which is not possible as this analysis ends in 2023.\n[Responses] We thank the reviewer for pointing this out. The original phrasing was indeed\nimprecise. We have revised the sentence (Line 158-160) to:\n[Simulated dust emissions in the Eastern Sources exhibited a non-monotonic trend—first\nincreasing and then declining over the past four decades (Fig. 2e).]\n[Comments] l. 174: The link between the sentence and extended figure 5 is not clear.\n[Responses] We appreciate the reviewer’s observation. The original reference to Extended Data\nFig. 5 was imprecise and may have caused confusion. We have removed this citation and now\nonly refer to Fig. 2d (Line 199), which clearly shows the net effect of vegetation on dust emissions\nin this area. The attribution of vegetation effects across different land cover types is discussed in\ngreater detail in the subsequent paragraph (Line 221) and illustrated in Extended Data Fig. 6, to\navoid redundancy and improve clarity.\n[Comments] l. 196-197: Extended Data Fig. 5b-d does not exist. Should be extended data fig.7.\nFig 1.a: wind speed and direction: are they annual averages?\n[Responses] We confirm that the correct reference should be Extended Data Fig. 7. This has been\ncorrected in the revised manuscript.\nThe wind vectors shown in Fig. 1a represent the spring (March–May) climatological mean\nwind speed and direction over 1982–2023, derived from ERA5 data. We have updated the figure\ncaption accordingly for clarity (Line 147).\n[Comments] l. 207: I don’t quite understand why this would be a paradox.\n[Responses] We agree that the original wording (“paradoxical”) might have overstated the\nsurprise of this observation. To improve clarity and avoid confusion, we have revised the sentence\nto directly state the pattern without labeling it as a paradox. The revised sentence (Line 237-239)\nnow reads: [Interestingly, dust emissions are higher in areas with weaker winds and sparse\nvegetation than in regions with stronger winds and denser vegetation (Supplementary Fig. 9)]\n[Comments] l. 210: unclear. Do you mean that land surface properties modulate wind regime?\n[Responses] We agree that the original phrasing was ambiguous. Our intention was not to suggest\nthat land surface properties (e.g., vegetation cover or surface roughness) modulate the wind\nregime itself (e.g., wind speed or direction), but rather that they modulate the effectiveness of\nwind in generating dust emissions. In other words, surface properties influence how susceptible\nthe surface is to wind erosion, not the wind dynamics themselves.\nTo clarify this point, we have revised the sentence as follows (Line 239-241):\n[...a critical vegetation threshold, beyond which bottom-up factors (e.g., vegetation cover and\nsurface roughness) limit the effectiveness of top-down drivers (e.g., wind speed) in generating\ndust emissions.]\n[Comments] l. 261/265: You mention a greening of drylands and desert contraction, however I\nam not sure that this is a global trend, as aridification also reaches large parts of the planet.\nPlease specify.\n[Responses] To avoid potential overgeneralization, we have revised the text to clearly indicate that\nthe greening trend and projected contraction apply to China, not to global drylands. The revised\nsentence (Line 290-293) now reads: [In China’s drylands, regional greening—driven by favorable\nclimate conditions, CO fertilization, and land-use optimization32–35—has, according to our\n2\nanalysis, contributed to a sustained reduction in dust activity.]\n[Comments] l.333: this statement would be more convincing if the analysis was restricted to a\nrestored area and compared to an unrestored one.\n[Responses] We agree that comparing restored and unrestored conditions enhances the credibility\nof the long-term impacts of ecological restoration. In the revised manuscript, we clarified that such\ncomparisons have already been incorporated in our analysis—for example, between greening and\nno-greening scenarios (Figs. 2e and 4), and across regions and vegetation types. These\ndemonstrate that restoration gains become more evident over extended periods. The revised text\n(Line 368-374) now better reflects this point.\n[Forward-looking principle: Developing a deep understanding of slowly evolving land surface\nconditions55, particularly changes in vegetation cover of dust sources, is essential. The long-term\nimpact of ecological restoration is evidenced by our comparison between greening and no-\ngreening scenarios (e.g., Fig. 2e and 4), as well as regional and vegetation-type differences in FVC\neffects. These findings demonstrate that the benefits of restoration become more pronounced over\nextended timescales.]\n[Comments] l.353-354: I am not sure that this has been demonstrated.\n[Responses] We agree that the original statement may have overstated our conclusion and could\nbe misread as a generalization of all previous studies. To address this, we have revised the\nsentence to more accurately reflect our intent.\nThe updated version (Line 399-404) reads: [While previous studies based on short-term\nobservational records have advanced our understanding of dust variability, they often cannot fully\ncapture the long-term effects of vegetation restoration due to the masking influence of atmospheric\nvariability. By offering process-based evidence across multiple timescales, our findings reveal that\nthe benefits of vegetation restoration become increasingly pronounced over longer periods.] This\nrevision clarifies that our interpretation applies specifically to studies relying on short-term data,\nand uses more cautious language to reflect the nuanced nature of our findings.\n[Comments] l. 791: cold availability -> code availability.\n[Responses] Corrected, thank you.\n[Comments] Supplementary Figure 8d: The display of data with a line between each point is\nconfusing, as there should not be a link between SSP1-2.6 “After 2050” and SSP2-4.5 “Before\n2050” dust emissions. Superimposing SSP could be more relevant.\n[Responses] Thanks for the suggestion. We have removed the connecting lines and now display\neach scenario as separate, unlinked points with error bars to improve clarity (see Supplementary\nFigure 16).\n[Comments] Do these prohibit publication or require revision?\nAccording to me, these concerns need major revisions by the authors.\n[Responses] We appreciate the reviewer’s detailed and constructive feedback. We have carefully\naddressed all major and minor concerns in the revised manuscript and believe that the revised\nversion meets the standards for publication.\n[Comments] Is the methodology sound? Does the work meet the expected standards in your field?\nAs far as I could assess, the methodology seems thorough. The model for dust emission was\nalready published in a previous paper, and relies on a large range of standard previous works\n(particularly studies by Y. Shao). Inputs and variables come from state-of-the-art datasets. In the\ndescription of the model, the total numbers of variables and of parameters of the model could be\nspecified.\n[Responses] As suggested, we have now specified the total number of variables and parameters\nused in the dust emission model in the Methods section. In response to reviewer feedback, we also\nclarified key model equations, added more site-based validation data, and expanded the number of\nsupplementary tables from 3 to 7 to provide full documentation of model parameters, inputs, and\nsupporting datasets. These additions improve the clarity and reproducibility of our approach.\n[Comments] Is there enough detail provided in the methods for the work to be reproduced?\nI was not able to download the data from : https://doi.org/10.11888/Terre.tpdc.301534.\n[Responses] We apologize for the issue with the data link. We have now provided an additional\nbackup link to the dataset via Figshare (https://figshare.com/s/fe3183386df90ca6d7d6) to ensure\nfull accessibility for reproducibility.\nReferences\nWu, C., Lin, Z., Shao, Y., Liu, X. & Li, Y. Drivers of recent decline in dust activity over East Asia.\nNat. Commun. 13, 7105, doi:10.1038/s41467-022-34823-3 (2022).\nZhang, P. et al. Abrupt shift to hotter and drier climate over inner East Asia beyond the tipping\npoint. Science 370, 1095–1099, doi:10.1126/science.abb3368 (2020).\nHessl, A. E. et al. Past and future drought in Mongolia. Sci. Adv. 4, e1701832,\ndoi:10.1126/sciadv.1701832 (2018).\nNilson, T. A theoretical analysis of the frequency of gaps in plant stands. Agric. Meteorol. 8, 25–\n38, doi:10.1016/0002-1571(71)90092-6 (1971).\nShao, Y., Klose, M. & Wyrwoll, K. H. Recent global dust trend and connections to climate forcing.\nJ. Geophys. Res.-Atmos. 118, 11107–11108, doi:10.1002/jgrd.50836 (2013).\nWe thank Reviewer #2 for their co-review in collaboration with a primary reviewer,\nand we appreciate Nature Communications’ initiative to support Early Career\nResearchers through this process.\n[Comments] This paper is comprehensive and focuses on the impact of greening on dust\nemissions in China.\nIt is scientifically sound and I hope this paper will be published.\nTo make it more accurate and reliable, I have some concerns and comments on the text.\nPlease find the comments throughout the text and look forward to your responses.\nBest wishes,\nReviewer.\n[Responses] Thank you for your positive and encouraging assessment of our work. When revising,\nwe have fully considered your thoughtful review and constructive suggestions, which have\nimproved the clarity, accuracy, and robustness of the manuscript.\n[Comments] l.38-39: Is this your result? Or do you have a reference for this claim?\n[Responses] Thank you for the comment. We have removed the sentence from the Abstract to\navoid overinterpretation of a specific numerical relationship. The key point is now clearly\naddressed in the main text, where we show that over 95% of dust emissions originate from regions\nwhere vegetation cover (FVC) is below 15%, and that dust emissions become much less sensitive\nto wind once this vegetation threshold is exceeded (see section “Threshold-dependent effects of\nvegetation on dust mitigation”) (Line 232). This supports the strategic importance of targeting\nlow-FVC drylands for mitigation.\n[Comments] l.92: What is this? Maybe Aridity Index.\n[Responses] Thank you for the suggestion. We have revised “arid index” to “Aridity Index” in the\ntext (Line 114). The label in Fig. 1c has also been updated accordingly for consistency.\n[Comments] l.208: Please clearly explain the <15% vegetation cover?\nIs it 15% green cover for an area that could be called 15% green area?\nOr the 15% increase in NDVI?\n[Responses] Thank you for your comment. We have clarified that the 15% threshold refers to\nregions where the Fractional Vegetation Cover (FVC) is less than 15%, rather than a 15% increase\nin NDVI. The revised sentence now explicitly defines this threshold, improving clarity (Line 242-\n244). [Over 95% of dust emissions originate from regions where fractional vegetation cover (FVC)\nis less than 15%. Beyond this threshold, dust emissions become significantly less sensitive to wind\nspeed.]\n[Comments] l.258-259: China's greening plan has been widely publicized in the media. It is also\npromising that your scientific works show the results. But there is still no accurate map of\ngreening activities showing the exact location of these activities. Is it possible to include an\naccurate and detailed map of these greening activities in your paper?\n[Responses] We appreciate Reviewer #3's suggestion to include a detailed map of greening\nactivities. While we agree this would be valuable, our study focuses on broad regional trends and\ntimescale-dependent processes. Mapping specific greening project locations is beyond the scope\nof our current analysis, but we acknowledge this as an important area for future work using\nhigher-resolution spatial data.\nIn the manuscript, we have utilized two high-quality land cover datasets (ESA CCI and\nMODIS) to quantify the enhanced contribution of different vegetation types to dust reduction (see\nExtended Data Fig. 6). While we provide maps of land cover change at regional scales, the spatial\nresolution does not allow detailed visualization of specific greening project locations. Moreover,\nExtended Data Fig. 7 presents the contribution of vegetation to dust decline across different\necoregions.\nAdditionally, Supplementary Figures 4e and 5 comprehensively illustrate the trends of\nvegetation greenness dynamics in the Eastern Asia dust source regions, reflecting both spatial and\ntemporal variations.\nGiven that our research emphasis is on timescale-dependent processes and broad regional\npatterns, and that the existing figures sufficiently support our conclusions, we have not included a\nmore precise map of greening activities. We agree that future work incorporating higher-resolution\nspatial data and exact greening project locations would be valuable to further elucidate the spatial\nlinkage between greening efforts and dust mitigation (Supplementary Discussion, Line 286-300).\n[Although the spatial resolution of products such as MODIS and AVHRR may miss fine-scale\ngreening details, the AVHRR dataset is notably affected by issues such as sensor degradation\nand orbital drift, especially around the early 2000s, which can introduce anomalies in the data—\neven though the GIMMS dataset has undergone corrections to mitigate these effects. Nevertheless,\nthe use of these continuous, long-term datasets enables assessment of decadal vegetation trends\nacross broad regions. For example, as shown in Fig. 2f, both GIMMS (analyzed over 2002–2015 to\nexclude the anomalously high value in 2001) and MODIS FVC products consistently indicate a\nsignificant increasing trend in vegetation’s contribution to reducing dust emissions, quantified as\napproximately +1 g m−2 per decade (P < 0.05) after 2001. Future research can expand on this\nfoundation by employing higher-resolution data (e.g., Landsat, Sentinel) and ground-based\nvalidation to more precisely attribute land management contributions. Our results provide a\ntheoretical basis for assessing the effectiveness of vegetation restoration—whether natural or\nanthropogenic—in regulating dust emissions across large spatial and temporal scales.]\n[Comments] Fig. 5: What is behind the threshold velocity that changes the dust emission?\nIs it just based on your research? Is it based on some laboratory or wind tunnel experiments?\nHow did you justify it?\n[Responses] Thank you for your comment. The term “threshold velocity” only appears in the\noriginal conceptual diagram (Fig. 5) to illustrate how vegetation affects dust emissions through\nsurface roughness and soil moisture. Although this concept is part of the model calculations\ndescribed in the Supplementary Methods, it is not explicitly mentioned or discussed in the main\ntext. To avoid potential confusion, we have removed “threshold velocity” from the figure and\nsimplified the diagram to focus on the vegetation–dust feedback mechanisms.\n[Comments] l. 521: DOD or similar products are good for estimating dust loads, but they are\nnot precise enough data for mapping dust emission sources. Dust plumes are very dynamic and\nDOD is also dynamic and cannot be easily related to dust emission sources. I would suggest\nlooking at these two papers.\nhttps://doi.org/10.1016/j.rsase.2022.100888\nhttps://doi.org/10.1016/j.rse.2009.03.002.\n[Responses] Thank you for your thoughtful comment and for recommending these valuable\nreferences. We fully acknowledge the importance of local-scale studies using DOD products or\nvisual interpretation techniques, which are particularly effective in identifying fine-scale and\nevent-specific dust sources. However, these approaches may face challenges when applied over\nlarge areas due to issues such as cloud contamination, surface reflectance, and the dynamic nature\nof dust plumes.\nIn contrast, our method is designed for continental-scale source attribution. By correlating\nmodeled emissions across the upwind region with downwind DOD observations in a major\nreceptor area, we identify large-scale source areas through statistically significant transport\nrelationships. This approach is better suited for revealing interannual or decadal-scale changes in\ndust emissions and their drivers (e.g., vegetation and soil moisture), especially in regions where\ndirect attribution based on local DOD is difficult or uncertain.\nThis point has been clarified in the revised Methods section (Line 600-607) and\nSupplementary Discussion (Line 263-272): [Caution should be exercised when attributing dust\nemissions using local DOD measurements or direct image interpretation, as both methods are\nsensitive to surface reflectance, cloud cover, and threshold settings, which can lead to\nmisidentifying transported dust as local emissions60,61. Our approach connects interannual\nvariations in dust emissions within upwind source regions—considering their driving factors such\nas climate and surface conditions—with downwind DOD measurements. This strategy enables the\ndetection of potential causal relationships and offers a physically grounded, spatially consistent\nframework suitable for large-scale analyses.]\n[The simulation of dust emissions depends on complex parameterizations and region-specific\ncalibrations, where minor adjustments can cause substantial outcome differences. To mitigate\nthese uncertainties when attributing driving factors, we used dust optical depth (DOD) in\ndownwind receptor areas as a proxy for dust emissions. This approach yielded conclusions\nconsistent with model-based results and outperformed traditional methods that rely on DOD\nobserved directly over source regions (see Methods). Its effectiveness stems from capturing\nintegrated dust transport signals, provided certain conditions are met—namely, a stable upwind–\ndownwind transport pathway, minimal local emissions in the receptor region, and favorable terrain\nand atmospheric conditions for dust deposition.]\n[Comments] l.525-529: As far as I know, the greening activities in China are not too large to be\nseen or evaluated by such low resolution satellite images of reanalysis data.\nYou made a claim that could be a reference for other areas. But your data are not as strong as\nexpected.\nI am afraid that your results may be related to some kind of natural greening or other natural\nfactors not seen in your work.\nI would suggest that you focus on making an accurate map of the greened areas by using field\ninformation and time series of Landsat and Sentinel imagery.\n[Responses] We have addressed the limitations of using coarse-resolution datasets such as ERA5-\nLand, MODIS, and AVHRR in our Supplementary Discussion (Lines 273-277 and 286-300),\nwhere we discuss their spatial resolution constraints and the implications for capturing fine-scale\ngreening activities.\n[Our study aims to determine whether long-term vegetation increases—regardless of whether\nthey arise from natural regeneration or ecological restoration—can sustainably influence dust\nemissions at the continental scale. Instead of differentiating between anthropogenic and natural\ngreening, we evaluate changes in vegetation cover as an integrated biophysical driver of dust\nvariability.]\n[Although the spatial resolution of products such as MODIS and AVHRR may miss fine-\nscale greening details, the AVHRR dataset is notably affected by issues such as sensor degradation\nand orbital drift, especially around the early 2000s, which can introduce anomalies in the data—\neven though the GIMMS dataset has undergone corrections to mitigate these effects. Nevertheless,\nthe use of these continuous, long-term datasets enables assessment of decadal vegetation trends\nacross broad regions. For example, as shown in Fig. 2f, both GIMMS (analyzed over 2002–2015\nto exclude the anomalously high value in 2001) and MODIS FVC products consistently indicate a\nsignificant increasing trend in vegetation’s contribution to reducing dust emissions, quantified as\napproximately +1 g m−2 per decade (P < 0.05) after 2001. Future research can build upon this\nfoundation by utilizing higher-resolution data (e.g., Landsat, Sentinel) and ground-based\nvalidation to refine attribution of land management effects. Our findings offer a theoretical basis\nfor assessing the effectiveness of vegetation restoration—whether natural or human-driven—in\nmodulating dust emissions across large spatial and temporal scales.]\nImportantly, our study focuses on providing a theoretical understanding of whether long-term\nvegetation increases, regardless of their origin, can exert sustained influence on dust emissions at\nthe continental scale. This broad, process-based perspective relies on consistent long-term datasets\nto reveal large-scale trends, rather than mapping localized greening patches.\nWe appreciate the suggestion to incorporate higher-resolution data like Landsat and Sentinel\nfor improved local-scale mapping, and have noted this as an important direction for future work.\n[Comments] l.599: Perhaps the same comment applies to the land cover maps.\nI would suggest applying the scale to each dataset. Of course, I can guess the scale of the maps\nand data based on the pixel size, but I am not sure that these data are appropriate for the greening\nin the study region.\nYou know, the scale/scope of greening in China is not clear, and this makes justification difficult\nand perhaps biased.\n[Responses] We also strengthened the justification for our conclusions by performing factorial\nexperiments, allowing us to isolate the individual contributions of vegetation and climate factors\nAs noted above, we acknowledge the limitations of coarse-resolution datasets in mapping\nlocalized greening efforts. In response, we have clarified in the Methods and Supplementary\nDiscussion that our study does not attempt fine-scale project attribution, but instead uses long-\nterm vegetation trends as a proxy to evaluate vegetation–dust interactions at continental scales. We\nagree that future work should incorporate higher-resolution land cover data and ground validation\nto refine spatial attribution and scale-specific interpretation.\n[Comments] l.605-607: (highlighted): “To address this issue, we employed a physical model of\ndust emission, DuEM v1, developed by Wu et al. to investigate the non-linear effects of these\ndriving factors on long-term dust variations.”\n[Responses] We agree that the attribution of the model should be made more explicit. In the\nrevised manuscript, we clarified the wording to avoid any potential ambiguity regarding\nauthorship. The revised sentence (Line 678-681) reads:\n[To address this issue, we employed a physically based dust emission model (DuEM v1),\ndeveloped by Wu et al. (2022)23, which explicitly represents nonlinear responses of dust flux to\nsurface wind speed, vegetation cover, soil moisture, and snow cover.]\nThis revision makes it clear that we are applying an existing model rather than presenting it\nas our own work.\n[Comments] You have written too much for each part of the paper. I would suggest shortening\nyour explanations, using flow charts with figures, and shortening the text.\n[Responses] In response to the concern about excessive text, we have shortened several\nexplanatory paragraphs and added a flowchart (Extended Data Fig. 1) to provide a clearer\noverview of our analyses. Thank you for the helpful suggestion. We have shortened several\nexplanatory paragraphs and revised the text to be more concise throughout the manuscript. To\nimprove clarity, we have also added an overview flowchart (Extended Data Fig. 1) to illustrate the\nstudy framework.\n[\nstations and reanalysis-based dust activity, used to evaluate simulated emissions and their response\nto driving factors. b, Overview of data types used in the study (see Supplementary Tables 2–7 for\ndetails). c, Key drivers of dust emissions across three relevant timescales. d–g, Main results,\nincluding climate teleconnections linked to intra- and interannual variability (d), partial\ncorrelations with local climate and vegetation conditions (e), physically-based dust emission\nsimulations and baseline experiments from 1982 to 2023 (f), and projections under CMIP6\nscenarios from 2015 to 2100 (g). Experimental design is described in Supplementary Table 1.]\n[Comments] I acknowledge the work done by the authors to take into account my comments, in\nparticular to demonstrate the robustness of the dust emission simulations and of MERRA DOD by\ncomparing them to stations observations. However, I still have issues with several aspects of the\nmanuscript.\nI provide below my comments with the associated line numbers from the Revised Manuscript.\n[Responses] We sincerely thank the reviewer for the positive evaluation and for recognizing our\nefforts to address the previous comments. We appreciate the constructive suggestions provided\nbelow and have carefully revised the manuscript accordingly.\n[Comments] My main concern deals with the way authors mention “vegetation restoration” all\nalong the manuscript, whereas their results only show the importance of wind and of vegetation\ncover on spring dust emissions. As also pointed by Reviewer 3, the authors do not perform any\ncomparison of “restored areas” versus “unrestored areas”. Consequently, this is misleading for\nthe reader, giving the impression that vegetation restoration will diminish dust emission in the\nfuture over the long-term, which is not what is demonstrated in the results. The results show that,\nfor the given climate conditions of the 12 CMIP6 models selected by the authors, vegetation\ngreening by 2100 will be large enough, given the concomitant wind intensity, to induce a\ndecreasing trend in the simulated dust emission. Besides, I am not totally convinced by the new\nperspective of the revised manuscript, focusing on the idea that wind drives short-term dust\nemissions, while vegetation drives their long-term dynamics. I think that it largely depends on the\nvegetation type, e.g. annual vegetation cover greatly depends on annual precipitation, which can\nexhibit a large interannual variability, and of wind conditions. This can be les striking for\nperennial vegetation and for woody cover. As the authors specify that 75% of the emitted dust\ncome from the Alashan Plateau, which is a semi-desert, it would be interesting to know which type\nof vegetation (annual, perennial, herbaceous, woody…) grows in that area.\n[Responses] We sincerely thank the reviewer for highlighting the imprecise use of \"vegetation\nrestoration\" in our initial manuscript. We recognize that this term carries the specific implication\nof\nintentional human-driven intervention, which differs from the scope of our analysis primarily\nfocused on demonstrating the broader impact of natural changes in vegetation cover (greening) on\ndust emissions. To ensure clarity and precision, we have rigorously revised the manuscript,\nreplacing \"vegetation restoration\" with \"vegetation greening,\" \"vegetation cover change,\" or\nsimilar terms throughout (see detailed responses below). This revision addresses the potential for\nmisinterpretation and better aligns our language with the scope of our findings.\nWe acknowledge that interannual climatic variability can mask the long-term effects of\ndrivers like vegetation greening. However, our analysis demonstrates that, over longer (multi-\ndecadal) timescales, the influence of vegetation greening becomes increasingly pronounced in a\nstatistically significant way. This is particularly evident in key dust source regions like the Alashan\nPlateau, where the presence of perennial woody and semi-woody shrubs plays a crucial role in\nphysically stabilizing surface soils against wind erosion, even under varying climate conditions.\ncohesion (Lines 219–233; Supplementary Table 2).\n[The ongoing greening of desert and steppe ecosystems in the Alashan Plateau, eastern Gobi\nDesert, and Ordos Plateau have greatly contributed to the decrease in dust emissions (Extended\nData Fig. 7a–d). Notably, more than 70 % of regionally accumulated dust originates from the\nAlashan Plateau, which only covers 30% of the Eastern Sources. This region is dominated by\nxerophytic desert vegetation composed mainly of perennial woody and semi-woody shrubs\nsuch as Reaumuria soongarica, Artemisia salsoloides, and Calligonum mongolicum (see\nSupplementary Table 2 for additional dominant vegetation types). The deep and persistent\nroot systems of these xerophytic shrubs act as ecological stabilizers, reinforcing surface soil\nstructure and mitigating erosion despite their sparse aboveground cover30. In contrast, the\nremaining four ecoregions, dominated by dry steppe and semi-arid grassland vegetation,\ncontribute relatively little to dust emissions. Nevertheless, observed water limitations combined\nwith increased grazing pressure (Extended Data Fig. 7e–g) may pose unexpected risks in the\nabsence of adequate management.]\nWe have also appropriately added discussions of vegetation types in other sections, such as\nLines 333–337. [Ensemble experiments integrating historical observations with CMIP6 scenario\nsimulations indicate that vegetation increasingly mitigates dust emissions over multi-decadal\ntimescales, as a result of greening in key dust source regions, including xerophytic deserts\ndominated by perennial woody and semi-woody shrubs such as those in the Alashan Plateau.]\n[Supplementary Table 2 | Dominant vegetation and ecosystem types in the Alashan Plateau.\nCumulative Dominant species Functional type Ecosystem type\narea group\n(%)\n0–10% Reaumuria soongorica, Artemisia Woody and semi- Desert\nsalsoloides woody perennial vegetation\nshrubs\n10–20% Reaumuria soongorica, Artemisia Woody and semi- Desert\nsalsoloides, Calligonum mongolicum, woody perennial vegetation;\nCultivated fields (spring wheat, rice, shrubs; Semi-shrub Cultivated\nsugar beet, sunflower, wolfberry, pear xerophytes; One-year vegetation\norchard) crops and deciduous\norchards\n20–30% Calligonum mongolicum, Hedysarum Woody and semi- Desert\nmongolicum, Artemisia salsoloides, woody perennial vegetation;\nPsammochloa mongolica, Reaumuria shrubs; Semi-shrub Cultivated\nsoongorica, Nitraria sphaerocarpa, xerophytes; One-year vegetation\nNitraria sibirica, Ephedra crops and deciduous\nprzewalskii, Sympegma regelii, orchards\nNitraria roborowskii, Cultivated\nfields (spring wheat, rice, sugar beet,\nsunflower, wolfberry, pear orchard)\n30–40% Reaumuria soongorica, Nitraria Woody and semi- Desert\nsibirica, Nitraria sphaerocarpa, woody perennial vegetation;\nSympegma regelii, Haloxylon shrubs; Semi-shrub Steppe\nammodendron, Calligonum xerophytes; Perennial vegetation;\nroborowskii, Tamarix ramosissima, grasses; One-year Cultivated\nArtemisia salsoloides, Potaninia crops and deciduous vegetation;\nmongolica, Stipa gobica, Stipa orchards; Temperate Forests\nklemenzii, Caragana tibetica, forest\nCultivated fields (spring wheat, rice,\nsugar beet, sunflower, wolfberry, pear\norchard), Forests (Cunninghamia\nlanceolata, Pinus khasya)\n40–50% Reaumuria soongorica, Nitraria Woody and semi- Desert\nsibirica, Nitraria sphaerocarpa, woody perennial vegetation;\nArtemisia ordosica, Artemisia shrubs; Semi-shrub Steppe\nsalsoloides, Haloxylon xerophytes; Perennial vegetation;\nammodendron, Anabasis brevifolia, grasses; One-year Meadows;\nSalsola laricifolia, Salsola crops and deciduous Cultivated\nabrotanoides, Calligonum orchards; Temperate vegetation;\nmongolicum, Sympegma regelii, Stipa forests; Deciduous Forests; Scrubs;\ngobica, Stipa klemenzii, Stipa scrubs; No vegetation No vegetation\nbreviflora, Caragana tibetica,\nPtilagrostis pelliotii, Sophora\nalopecuroides, Poacynum\nhendersonii, Glycyrrhiza inflata,\nAlhagi pseudoalhagi, Karelinia\ncaspica, Tamarix ramosissima,\nPopulus davidiana, Betula\nplatyphylla, Cultivated fields (spring\nwheat, rice, sugar beet, sunflower,\nwolfberry, pear orchard), Bare ground\n/ No vegetation\nNote: The data are derived from the Vegetation Map of the People's Republic of China\n(1:1,000,000), compiled by the Chinese Academy of Sciences and publicly available via the\nResource and Environment Data Cloud Platform (RESDC,\nhttps://www.resdc.cn/data.aspx?DATAID=122). Vegetation polygons in the vector dataset were\nranked by area, and for each successive 10% cumulative area group, the dominant species,\nfunctional types, and ecosystem types were summarized.]\n[Comments] These misleading mentions can be found in: The title: “Vegetation restoration drives\nlong-term dust mitigation in Eastern Asia”. This needs to be changed as you did not perform\nanalysis dedicated to the comparison of “restored” versus “unrestored” areas, accounting for the\nvegetation type of these areas. The title could be changed for “vegetation cover” but should also\nbe nuanced as the long-term impact of vegetation cover on dust emission depends on the\nvegetation type and on its resilience to potentially unfavorable climate conditions.\n[Responses] We have revised the title to \"Vegetation greening drives long-term dust mitigation in\nEastern Asia\" to more accurately reflect the focus of our analysis, which examines the general\nimpact of vegetation cover changes rather than specific, targeted restoration projects. This change,\nalong with the consistent use of \"vegetation greening\" or \"vegetation cover\" throughout the\nmanuscript, ensures a more accurate and nuanced representation of our findings. This revised title\nbetter captures the scope of our study, which focuses on the observed and simulated vegetation\ntrends (i.e., greening) and avoids implying a direct comparison between restored and unrestored\nareas.\n3\n[Comments] l. 40: in the abstract: “Our results demonstrate that vegetation restoration\nsuppresses long-term dust emission”.\n[Responses] We have replaced “vegetation restoration” with “vegetation greening” in the abstract\n(Line 37). We also revised the sentence to improve logical flow and clarity. The original text:\n[The contribution of vegetation is highly uneven. Targeting regions with less than 15%\nvegetation cover, which contribute 95% of dust emissions, offers the greatest mitigation potential.\nOur results demonstrate that vegetation restoration suppresses long-term dust emissions by\nstabilizing the land surface in arid and semi-arid regions, even under a warming climate.]\nnow reads (Line 37-40):\n[The contribution of vegetation is highly uneven: greening in sparsely vegetated regions\n(<15% cover; responsible for 95% of dust emissions) offers the greatest mitigation potential by\nstabilizing the land surface and suppressing long-term emissions, even under a warming climate.]\n[Comments] l. 54: “vegetation loss”-> I think you mean vegetation cover. “Loss” means that\nsomeone or something actually deprives the land surface of its vegetation cover. Also l. 65\n“vegetation loss”.\n[Responses] For Line 54 (now Line 52), we have replaced “vegetation loss” with “changes in\nvegetation cover” to provide a more accurate and neutral description of changes in vegetation\nextent, without implying active removal. [Although both climate variability and land surface\nchanges, particularly changes in vegetation cover, are recognized as major drivers of dust\nactivity across global drylands5,8–11, surprisingly, their relative contributions to dust variability\nremain unclear.]\nFor Line 65 (now Line 62), the term refers to general vegetation cover rather than a loss\nevent, so we have revised it to “vegetation cover” to accurately describe the state of the land\nsurface without implying degradation. [Dust emissions are often examined within distinct\ndisciplinary frameworks16: paleoenvironmental and sedimentary studies tend to emphasize long-\nterm changes in surface conditions, such as vegetation cover and soil erodibility (i.e., how easily\nmineral particles can be picked up and transported by wind), which are influenced by climatic\ntrends over decades to centuries17,18, while meteorological analyses typically focus on interannual\nvariability, often by analyzing specific years or extreme events driven by surface winds, droughts,\nand large-scale circulation patterns8,19,20.]\n[Comments] Idem l.56: “recovery”. I think you actually mean “greening” or “re-greening”, which\ncan be due e.g. to climate trends.\n[Responses] We have removed the term “recovery” and clarified the expression by merging it into\nthe preceding phrase “changes in vegetation cover” to more accurately describe vegetation\ndynamics without implying human-driven restoration. The sentence (Lines 51–54) now reads:\n[Although both climate variability and land surface changes, particularly changes in\nvegetation cover, are recognized as major drivers of dust activity across global drylands5,8–11,\nsurprisingly, their relative contributions to dust variability remain unclear.]\n[Comments] l.59: It is not that much “unexpected” as dust emission largely depends on wind\n4\nspeed.\n[Responses] We agree that the interplay between vegetation and wind is crucial. We have revised\nthe text (Lines 54–58) to emphasize that the heterogeneous effects of vegetation arise from the\ncomplex interaction between vegetation cover and wind-driven dust emissions.\nThis revision ensures a more accurate representation of the relative influences of land surface\nprocesses and atmospheric drivers. The updated passage (Lines 54–59) now reads:\n[Substantial vegetation changes have been observed in drylands12, but their effects on dust\nactivity remain highly heterogeneous due to interactions with wind-driven processes13–15.\nDisentangling the relative influence of land surface processes and atmospheric drivers is therefore\ncritical to understanding dust variability and informing large-scale land management strategies.]\nThis revision preserves the original references (13–15) while clarifying that the\nheterogeneous effects arise from the complex interaction between vegetation cover and wind,\nimproving the logical flow between the two sentences.\n[Comments] l. 65: “land degradation”. This is a complex concept that does not have a consensual\nand clear definition, neither among international institutions nor among scientists. How do you\ndefine it?\n[Responses] we have replaced the term “land degradation” with a more specific and neutral\ndescription (Line 61-62):\n[Dust emissions are often examined within distinct disciplinary frameworks16:\npaleoenvironmental and sedimentary studies tend to emphasize long-term changes in surface\nconditions, such as vegetation cover and soil erodibility (i.e., how easily mineral particles can\nbe picked up and transported by wind), which are influenced by climatic trends over decades to\ncenturies17,18; while meteorological analyses typically focus on interannual variability, often by\nanalyzing specific years or extreme events driven by surface winds, droughts, and large-scale\ncirculation patterns8,19,20.]\nThis wording avoids the ambiguous concept of degradation while clearly reflecting the\nfactors relevant to dust emissions in both paleoenvironmental and sedimentary studies.\n[Comments] l. 70: “driven by”. Actually the study by Mulitza mentioned a parallel between an\nincreased dust deposition and human activities. They did not state that humans were a driver of\ndust emission.\n[Responses] We carefully checked the study by Mulitza et al. (2010) and revised the sentence\n(Line 68-70) to accurately reflect their findings:\n[For example, in the Sahel, dust activity rose sharply in the 19th century, coinciding with\nboth drying trends and agricultural expansion21.]\nThis wording avoids implying a direct causal relationship between human activities and dust\nemissions, consistent with the original study.\nReferences\nMulitza, S., Heslop, D., Pittauerova, D. et al. Increase in African dust flux at the onset of\n5\ncommercial agriculture in the Sahel region. Nature 466, 226–228, doi:10.1038/nature09213\n(2010).\n[Comments] l.321: this actually depends on vegetation type and the associated interannual\nvariability of the vegetation cover.\n[Responses] We appreciate the reviewer's point regarding the importance of vegetation type and\nits interannual variability. We have revised the text (Lines 33–337) to more explicitly address this,\nnoting that the mitigation of dust emissions by vegetation depends on factors such as the specific\ntype of vegetation (e.g., perennial woody shrubs vs. annual grasses) and its resilience to climate\nfluctuations. This underscores the importance of accounting for these factors when assessing the\nlong-term influence of vegetation on dust emissions.:\n[Ensemble experiments integrating historical observations with CMIP6 scenario simulations\nindicate that vegetation increasingly mitigates dust emissions over multi-decadal timescales, as a\nresult of greening in key dust source regions, including xerophytic deserts dominated by\nperennial woody and semi-woody shrubs such as those in the Alashan Plateau. By contrast,\nshort-term fluctuations in dust emissions are primarily driven by interannual variations in\nvegetation cover and wind conditions. These results underscore that robust quantification of the\nlong-term influence of vegetation on dust emissions requires extended observations and careful\ntimescale separation; otherwise, the influence of slowly evolving environmental drivers on multi-\ndecadal dust trends could be overlooked.]\n[Comments] l. 354: as you did not perform any analysis of “restored” areas versus “unrestored”\nareas, I do not think that you can state that the decorrelation between soil moisture and vegetation\ncover is related to restoration projects.\n[Responses] We appreciate the reviewer’s insightful comment. In the revised manuscript, we have\nremoved the previous attribution of the decoupling between vegetation greenness and water\navailability to restoration projects. The corresponding text (Lines 360–364) has been revised to\nread:\n[Feedbacks between vegetation and dust emissions in drylands are shaped by interactions\namong climate fluctuations, human interventions, and intrinsic vegetation traits. In Mongolian\ngrasslands, persistent severe droughts which are regionally associated with elevated dust\nemissions (Fig. 2), have constrained vegetation growth43–45, thereby amplifying a positive\nfeedback between vegetation browning and dust generation46. However, this negative trend can be\nmitigated through targeted human interventions47,48, such as the \"Grain for Green\" project\nlaunched in 2000, which aimed to reduce dust emissions in desertification-prone regions. Despite\ndeclining water availability, the Eastern Sources as a whole have experienced increasing\nvegetation greenness, indicating a decoupling between vegetation dynamics and immediate\nwater supply (Supplementary Fig. 6a), likely driven by improved water-use efficiency\nthrough reduced stomatal conductance49. These findings underscore the importance of\nconsidering human interventions, climatic forces, and vegetation physiology together when\nevaluating dust variability and designing mitigation strategies, particularly for xerophytic\nherbaceous and shrub communities that stabilize soils against wind erosion.]\ndiscussion to highlight ecological mechanisms and potential policy implications for dust\nmitigation in drylands.\n[Comments] l. 370: “the long term impact of ecological restoration is evidenced by our\ncomparison between greening and no-greening scenarios” and l. 373 “the benefits of restoration”:\nthis is very misleading for the reader. As far as I understood, you performed simulations with and\nwithout vegetation cover, which is different from simulations accounting or not for vegetation\nrestoration projects. Your results mostly illustrate the importance of the vegetation cover (in the\nbroad sense) for wind erosion.\n[Responses] We have revised the sentence (Lines 383–384) as suggested. It now reads:\n[Forward-looking principle: Developing a deep understanding of slowly evolving land\nsurface conditions55, particularly changes in vegetation cover of dust sources, is essential. The\nlong-term impact of vegetation dynamics is evidenced by our comparison between greening\nand no-greening scenarios (e.g., Fig. 2e and 4), as well as regional and vegetation-type\ndifferences in FVC effects, suggesting that vegetation greening, whether natural or human-\nfacilitated, increasingly reduces dust emissions over multi-decadal timescales.]\nAdditionally, to avoid any misunderstanding, we clarified in the Methods (Experiments 1 and\n2) that the “no-greening” scenario keeps vegetation at first-year values with seasonal variation,\nisolating its long-term effects rather than removing the vegetation parameter, which would\notherwise produce unrealistic differences (Line 856-860).\n[Experiment 1: For this experiment, we focused exclusively on the variations of the target\nvariable and kept the remaining variables fixed at their values from the first year of the data series.\nGiven the seasonal nature of dust emission, we accounted for intra-annual variations in these\nvariables and excluded their interannual changes, as outlined by Wu et al.23. This helped us\nunderstand how specific variables drive dust emissions independently of long-term trends.\nExperiment 2: In this experiment, we isolated the effect of vegetation by holding it\nconstant at its values from the first year, while allowing other variables—such as friction\nvelocity, soil moisture, and snow cover—to vary over time. Seasonal variations in vegetation\nwere still included, so friction velocity could respond to seasonal vegetation structure. By\ncomparing the results of this experiment with the baseline, where all factors were allowed to vary,\nwe were able to quantify the specific contribution of vegetation to dust emissions. This approach\nstrengthened the robustness of our results by clearly distinguishing the role of vegetation from\nother influencing factors.]\n[Comments] l. 401: “long-term effects of vegetation restoration”: there are two misleading elements\nhere. First, vegetation cover can itself exhibit a large interannual variability, as does climate and\nwind speed. This largely depends on the vegetation type (e.g. annual or perennial, trees or grass…).\nSecond, your results illustrate the effect of vegetation cover on dust emission, but you did not\nperform specific analyses comparing “restored” and “unrestored” areas.\n[Responses] We have revised the sentence to avoid overstating the role of “vegetation restoration”\nand to clarify the influence of vegetation variability and atmospheric masking. The revised text\n(Lines 429) now reads:\n[Our analysis helps to clarify the long-term (interdecadal or even longer) influence of\nvegetation cover on dust variability, which is often masked by atmospheric fluctuations. By\noffering process-based evidence across multiple timescales, our findings reveal that the benefits of\nvegetation greening become increasingly pronounced over longer periods.]\nThis revision adopts a more neutral phrasing that acknowledges prior research while\naccurately reflecting the scope and implications of our findings.\n[Comments] My second general comment deals with the number of figures. There are still a huge\nnumber of figures/panels (37 panels in Main, 67 panels in Extended Data, 104 panels in\nSupplementary, thus more than 200 panels altogether). My opinion is that this actually overwhelm\nthe reader and makes us loose the central message of this work. Besides, maps figures are really\nhardly readable. They could be reframed to the study area (90°E-120°E, 30°N-50°N) instead of 70°\nE-130°E, 25°N-55°N. I think that some of them are redundant or maybe unnecessary. For\nexample, I wonder whether Ext. Data Fig. 3 is really necessary.\n[Responses] We sincerely thank the reviewer for their constructive feedback regarding the number\nof figures and their impact on readability. We recognize that the initial manuscript contained a\nlarge number of figures, potentially overwhelming the reader and obscuring the central message.\nWe have carefully addressed this concern by reducing the overall number of figures and\nstreamlining the presentation of information.\nIn direct response to the reviewer's comments, we have made the following specific changes:\nRemoval of Redundant Figures:\nWe removed 1 figure from the Main Figures, 1 figure from Extended Data, and 5 figures from the\nSupplementary Figures.\nSpecifically,\nFigure 5 Conceptual diagrams illustrating the vegetation-dust feedbacks was removed\nbecause the vegetation–dust feedback mechanisms it illustrated were not essential for supporting\nthe main conclusions. While such feedbacks are conceptually important, they are already well\nsupported by cited studies (Lines 353–368), and their complexity cannot be fully captured in a\nsingle schematic.\nExtended Data Fig. 9 Baseline experiments on the contributions of major factors to dust\ndecline from 2001 to 2017 was removed because it primarily showed the spatial patterns from\nExperiment 3, which were not essential for supporting the main conclusions. The core\nmethodology and results, including the quantified contributions of major drivers to the dust\ndecline and the threshold-dependent vegetation responses, are already clearly presented in\nSupplementary Table 1 and Fig. 3.\nSupplementary Fig. 5 Changes in greenness in Eastern Asian sources of dust was removed\nbecause it largely overlaps with Supplementary Fig. 4, which already illustrates similar vegetation\n8\ngreening patterns given the strong correlation between FVC and NDVI.\nSupplementary Fig. 9 Contribution of vegetation to dust emission across gradients of wind\nspeed and vegetation trends was removed because it largely overlapped with Fig. 3 and\nSupplementary Fig. 4, and was therefore not essential for supporting the main conclusions].\nSupplementary Fig. 14 Spatial patterns of the effects of constraining vegetation and soil\nmoisture on wind-driven dust emission was removed because its results, though informative, did\nnot add new insight beyond those already presented in Fig. 3, and thus were not essential to the\nmain conclusions.\nSupplementary Fig. 20 Relationships between DuEM dust emissions and local and\ndownwind MERRA-2 dust optical depth (DOD) was removed because its purpose was to\nevaluate whether DOD in the Eastern Sources or in downwind regions was more strongly\ncorrelated with dust emissions, thereby supporting our use of downwind DOD in subsequent\nstatistical analyses as a complement to dust storm observations (given that station records are only\navailable for 2001–2023). This comparison has now been incorporated into Supplementary Figure\n14c, where we validate both regional DOD datasets against observed dust storm days.\nSupplementary Fig. 21 Comparison between time of peak dust optical depth (TPDOD) in\ndownwind regions and the green-up date (GUD) in Eastern Sources during 1982–2022 was\nremoved because this analysis served only as supplementary information illustrating the potential\ninfluence of vegetation phenology on dust emissions. It is not central to the study’s main\nconclusions and could potentially overwhelm readers without offering additional insight into the\nkey mechanisms discussed.\nMerging of Figures:\nSupplementary Fig. 7 The contribution of snow cover to the trends of dust emission during\n1982–2023 was merged with Supplementary Fig. 6 Patterns of duration of snow cover and its\ncontribution to dust emission in Eastern Sources during 1982–2023, now renumbered as\nSupplementary Fig. 5, to streamline presentation and avoid redundancy.\nSupplementary Fig. 11 Relationship between vegetation cover (FVC) and soil organic carbon\nconcentration was merged with Supplementary Fig. 10 (now Supplementary Fig. 7), as both\nillustrate spatial variations in soil properties along FVC gradients relevant to the threshold-\ndependent effects of vegetation on dust emissions.\nAdjustments to Map Extents:\nSupplementary Fig. 3 Partial contributions of factors to dust activity in Eastern Sources and\ndownwind regions affected by dust was adjusted to focus more specifically on the Eastern\nSources. Since this analysis reflects teleconnections, areas that are not physically related have\nbeen excluded to improve clarity.\nFor most figures in this study, however, the current spatial frame (70°E–130°E, 25°N–55°N) has\n9\nbeen intentionally maintained because it captures the full spatial context of the East Asian dust\nsystem, encompassing both the core emission sources and adjacent transport regions. This broader\nframing is essential for illustrating the geographical positioning of the Alashan Plateau and other\nmajor dust sources relative to downwind affected areas, thereby enhancing the interpretability and\npolicy relevance of the results. Narrowing the map to the immediate study area would obscure\nthese spatial linkages and reduce the clarity of regional dust transport patterns. This rationale\nfollows the regional presentation adopted by Wu et al. (2022, Nature Communications), which\nalso employed a broader East Asian domain while focusing its main findings on the Eastern\nSources, thus maintaining physical consistency between source dynamics and large-scale impacts.\nFigure Revision and Clarification:\nExtended Data Fig. 3 Global consistency between annual peak and average dust emissions\nhas been retained but revised. Together with Extended Data Fig. 2, these results provide a solid\nbasis for focusing our analysis on the spring dust peak in Eastern Asia. To address the reviewer’s\nconcern, we removed the original panel c and unrelated details, and added a new comparison\nbetween the interannual variations of peak DOD and annual DOD over the Eastern Sources,\nshowing a significant correlation (R2 = 0.59, P = 3.95e−9). To clarify the broader significance of\nthis figure, we explicitly highlight in the figure legend that: [These results demonstrate a strong\nspatial and temporal consistency between annual and peak dust emissions, thereby supporting the\nuse of the spring dust peak in East Asia as a representative metric and providing a broadly\napplicable framework for other regions.]\nWe believe these revisions have significantly improved the clarity and focus of the manuscript,\nallowing the reader to more easily grasp the key findings and their implications.\n[\nExtended Data Fig. 3 | Global consistency between annual peak and mean dust optical depth\n(DOD). a, The multiyear mean of MERRA-2 DOD (scaled by × 10−4). b, The annual peak of\nhatched areas indicate regions with significant correlations (P < 0.05) between DOD and PDOD.\nc–e, The multiyear variations and correlations of DOD and PDOD in the Northern Hemisphere\n(c), Southern Hemisphere (d), and the Eastern Sources of East Asia (e). For the Eastern Sources,\nthe spring (March–May) DOD is used to represent the seasonal dust peak to ensure consistency\nwith the definition adopted in the main text. *** indicates P < 0.001. These results demonstrate\na strong spatial and temporal consistency between annual and peak dust emissions, thereby\nsupporting the use of the spring dust peak in East Asia as a representative metric and\nproviding a broadly applicable framework for other regions.]\nReferences\nWu, C., Lin, Z., Shao, Y., Liu, X. & Li, Y. Drivers of recent decline in dust activity over East Asia.\nNat. Commun. 13, 7105, doi:10.1038/s41467-022-34823-3 (2022).\nOther minor comments:\n[Comments] l. 114: “ranked as the strongest drivers” -> “ranked among the strongest drivers”?\n[Responses] OK, we have revised the sentence to “ranked among the strongest drivers” (Line 112)\nas suggested.\n[Comments] l. 115: Fig 1d does not show soil moisture but precipitation.\n[Responses] We thank the reviewer for this helpful comment. We have revised “soil moisture” to\n“moisture availability (precipitation or soil moisture)” (Line 114-115) to clarify that both variables\nwere among the strongest drivers identified in Fig. 1d and Fig. 1e. [Similarly, surface wind speed,\nNDVI, and moisture availability (precipitation or soil moisture) ranked among the strongest\ndrivers of dust emissions in the partial correlation analysis (Fig. 1d, e).]\n[Comments] L 226: “in associate with higher livestock density” -> “while also experiencing”?\n[Responses] Thank you for the comment. We have removed the causal expression linking\nlivestock density to dust emissions and updated Extended Data Fig. 7 accordingly. Detailed\ninformation on these modifications is provided in the following response.\n[Comments] l. 227: Ext. Fig. 7e-g do not really show a dynamic through time, excepted 7f bottom\nwhich show a difference between 2010-2015.\n[Responses] We appreciate the reviewer’s helpful comment. We have revised the description of\nwater limitation in the main text to remove any reference to temporal dynamics. The original\npanels f and g have been replaced, as they only showed gradient variations and did not adequately\ncapture causal relationships between grazing pressure and dust emissions. To maintain consistency\nwith panel e, in the revised Extended Data Fig. 7, panels f and g now illustrate the spatial\npatterns of water limitation and grazing pressure across different ecoregions. In addition, we\nupdated the grazing pressure data by comparing 2010 and 2020 rather than 2010 and 2015.\nAccordingly, the related text in the main manuscript (Lines 229–233) now reads:\n[Notably, more than 70 % of regionally accumulated dust originates from the\nAlashan Plateau, which only covers 30% of the Eastern Sources. This region is dominated by\nxerophytic desert vegetation composed mainly of perennial woody and semi-woody shrubs\nsuch as Reaumuria soongarica, Artemisia salsoloides, and Calligonum mongolicum (see\nSupplementary Table 2 for additional dominant vegetation types). The deep and persistent root systems of these\nshrubs effectively stabilize surface soils despite sparse vegetation cover. In contrast, the\nremaining four ecoregions, dominated by dry steppe and semi-arid grassland vegetation,\ncontribute relatively little to dust emissions. Nevertheless, observed water limitations\ncombined with increased grazing pressure (Extended Data Fig. 7e–g) may pose unexpected\nrisks in the absence of adequate management.]\n[\nExtended Data Fig. 7 | Assessment of vegetation's contribution to dust emission and risk\ndiagnosis based on ecoregions in Eastern Sources during 2001–2023. a, Distribution and\nlandscape overview of the five ecoregions. b, Trends in vegetation cover (FVC) and its\n12\ncontribution to dust emission during 2001–2023. c, Total emissions of dust in different ecoregions.\nd, Contribution of vegetation to trends of emission in different ecoregions. e, Potential limitations\nof water and grazing on vegetation growth. The upper part of (e) shows the Pearson correlation\ncoefficient between the drought indicator (scPDSI) and NDVI (water limitation index29), where a\nvalue greater than 0 indicates water limitation, and a value less than 0 indicates water surplus. The\nlower part of (e) represents difference of grazing pressure in 2020 compared to 2010. f–g, Spatial\ndistributions of the water limitation index (f) and grazing difference (g) across ecoregions. Box\nplots show the interquartile range (25th–75th percentiles); whiskers represent the mean ± 1 SD,\nand the horizontal line denotes the median. * and oblique lines indicate P < 0.05.]\n[Comments] l. 231: what do you mean by “vegetation cover […] governing surface soil\nmoisture”? The link between vegetation and soil moisture in drylands is not straightforward, see\ne.g. Deng et al 2020 and Gonsamo et al 2021:\nDeng, Y., Wang, S., Bai, X., Luo, G., Wu, L., Chen, F., ... & Lu, Q. (2020). Vegetation greening\nintensified soil drying in some semi-arid and arid areas of the world. Agricultural and Forest\nMeteorology, 292, 108103.\nGonsamo, A., Ciais, P., Miralles, D. G., Sitch, S., Dorigo, W., Lombardozzi, D., ... & Cescatti, A.\n(2021). Greening drylands despite warming consistent with carbon dioxide fertilization effect.\nGlobal Change Biology, 27(14), 3336-3349.\n[Responses] We thank the reviewer for highlighting the complex relationship between vegetation\ncover and soil moisture in drylands. We fully acknowledge that vegetation may not\nstraightforwardly “govern” surface soil moisture, as feedbacks and regional differences can lead to\ndifferent responses (e.g., Deng et al., 2020; Gonsamo et al., 2021). Following the reviewer’s\nsuggestion, we have revised the text to remove the previous phrasing implying direct vegetation\ncontrol over soil moisture. The updated description (Lines 240–243) now reads:\n[The path model for the interannual variability in dust activity reveals that wind speed exerts\nthe strongest direct effect (Extended Data Fig. 8a, b). Our model experiments (Experiment 3,\nSupplementary Table 1) show that surface wind (80.7%) and vegetation cover (20.11%) together\ncontributed most to the observed reduction in dust emissions from 2001 to 2017 (Fig. 3a–c),\nsupporting previous findings23. Vegetation also indirectly affects dust emissions through its\nmodification of surface conditions, which in turn alter near-surface wind dynamics and soil-\nmoisture availability (Extended Data Fig. 8c).]\n[Comments] l. 239: Suppl. Fig 9 shows NDVI trends, not absolute values, thus it does not\nillustrate the “regions with denser vegetation”.\n[Responses] We thank the reviewer for this comment. Supplementary Fig. 9 merely reflects the\ncorrelation between vegetation gradients and dust, rather than the actual physical processes. As the\noriginal sentence mainly served as a transitional statement, we have removed Supplementary\nFig. 9 along with the corresponding text. The main sentence at Line 244-248 has been revised\nto: [Spatial analyses reveal pronounced heterogeneity in the response of dust emissions to\nvegetation changes (Fig. 3). This pattern arises from a critical vegetation threshold, beyond which\nbottom-up factors (e.g., vegetation cover and surface roughness) limit the effectiveness of top-\n13\ndown drivers (e.g., wind speed) in generating dust emissions.] This revised paragraph, together\nwith the preceding text, supports the observed spatial and interannual influence of vegetation on\ndust emissions.\n[Comments] l. 247: Suppl. Fig. 13 shows land cover types. Thus I am not sure that Suppl. Fig. 13\n& 14 illustrate “grassland expansion, contraction, or shrub encroachment”.\n[Responses] Thank you for the comment. We have updated Supplementary Fig. 13 (now\nSupplementary Fig. 9). The former Supplementary Fig. 14, as mentioned earlier, has been\nremoved. In the main text, when discussing the threshold-dependent effects of vegetation, we\nfound that grasslands are primarily distributed near the critical FVC threshold. Their distribution\nmay influence the extent of key dust source areas, as regions with FVC < 15 account for 95% of\ndust emissions in the study area. To illustrate the potential implications, we examined changes in\ngrassland area and accordingly updated Supplementary Fig. 9. The corresponding text (Line 253-\n258) has been revised as follows: [Near this threshold, grassland dynamics—such as expansion,\ncontraction, or shrub encroachment—reflect potential changes within the sub-threshold range\n(Supplementary Fig. 9) and play a key role in modulating dust emission responses. Observed\nincreases in grassland area and FVC over bare soil may enhance surface stabilization and suppress\ndust emissions (Extended Data Fig. 6 and Supplementary Fig. 9c–f).]\n[\nEastern Sources. a, Area of each land-cover type per FVC (%) bin (×103 km2), from MODIS\n(thick line) and ESA-CCI (thin line); dashed line indicates the FVC = 15 threshold where dust\nemissions sharply decline. b, Grazing pressure (sheep, cattle, goat, head km−2) per FVC bin in\n2010 (thin line) and 2020 (thick line). c–f, Annual grassland distribution across FVC bins: MODIS\n(c, d) and ESA-CCI (e, f); panels d and f zoom in on FVC < 15 to show interannual trends. ***\nindicates P < 0.001. These results highlight that the key dust source areas with FVC < 15 are\ninfluenced by grassland changes, showing increasing trends in both grassland extent and the\ngrazing livestock they support within these regions.]\n[Comments] l. 250: “degradation” of what? How do you define it? Do you mean decrease in\nFCV?\n[Responses] We appreciate the reviewer’s helpful comment. We agree that the term “degradation”\nwas vague in this context. To clarify our meaning, we have replaced it with “reduction in FVC” in\nthe revised manuscript (Line 258-260), as our analysis specifically refers to decreases in\nvegetation fractional cover within the sub-threshold regions. The revised sentence now reads:\n15\n[Ecoregions within this sub-threshold zone, including the Alashan Plateau, warrant particular\nattention, as any reduction in FVC there cannot be offset by improvements elsewhere.]\n[Comments] l. 273-274: this depends on the vegetation cover (lower or greater than 15%).\n[Responses] We thank the reviewer for the comment. We have clarified the description to specify\nthat vegetation here refers to areas within dust source regions. The revised sentence (Lines 286–\n289) now reads: [This shift in dominant drivers is further supported by EEMD-based timescale\ndecomposition (Supplementary Fig. 15), which reveals that wind signals dominate at shorter\ntimescales (e.g., IMF1–IMF3), while vegetation within dust source regions emerges as the\nleading driver at longer timescales (IMF4 and above). This pattern primarily reflects the\ninfluence of vegetation within areas of FVC below ~15%, where dust emissions are most\nsensitive to change.]\n[Comments] l. 283: “expands” has a spatial implication -> Do you mean “increase”?\n[Responses] Thank you for the comment. We have replaced “expands” with “increases” to avoid\nthe spatial implication (Line 298).\n[Comments] l. 288: which type of vegetation? Trees? Bush? Grass? Annual? Perennial? Crops?...\n[Responses] Thank you for the comment. We have clarified the vegetation types in the manuscript\n(Line 302-307). [Vegetation resilience, particularly of perennial woody and semi-woody shrubs in\ndesert regions (e.g., the Alashan Plateau) and perennial grasses in semi-arid steppe regions (e.g.,\nthe Mongolian Grassland), underpins the multi-year mean vegetation status and essential\necosystem services32, hence buffering against short-term fluctuations in dust emissions.]\n[Comments] l. 290: “expansion” means that the spatial extent of vegetation cover increases. Do\nyou mean “vegetation cover increase” (for a given space)? Besides, this sentence should be moved\nto introduction or conclusion (or deleted).\n[Responses] Thank you for the comment. We agree that the original sentence is redundant and\nhave removed it to streamline the text.\n[Comments] l. 293: this sentence deals with past decades, whereas the section deals with future\nprojections.\n[Responses] We thank the reviewer for the comment. We have revised the sentence to clarify that\nthe projected continuation of regional greening occurs within dust source regions and may\ncontribute to a sustained reduction in dust activity. The updated sentence (Line 307-309) now\nreads:\n[In China’s drylands, regional greening over past decades—fueled by favorable climate\nconditions, CO fertilization, and land-use optimization33–36—is projected to continue within\n2\ndust sources (Extended Data Fig. 9). Separate projections indicate that China’s dryland extent,\ndefined as regions with an Aridity Index below 0.5, may decline by 9.3%, 13.4%, and 17.3% by\n2050, 2070, and 2100, respectively37. These trends support our predictions and underscore the\npotential of gradual desert contraction to mitigate dust emissions.]\n[Comments] l. 294: “China’s dryland extent”: defined by what? Do these projected numbers\naccount for “restoration projects”?\n[Responses] Thank you for the comment. We have clarified the definition of China’s dryland\nextent in the revised text (Line 310-311):\n[Separate projections indicate that China’s dryland extent, defined as regions with an\nAridity Index below 0.5, may decline by 9.3%, 13.4%, and 17.3% by 2050, 2070, and 2100,\nrespectively37. These trends support our predictions and underscore the potential of gradual desert\ncontraction to mitigate dust emissions.]\n[Comments] l. 349: ref 44 to 46 do not mention “land degradation”.\n[Responses] Thank you for the comment. We have removed the term “land degradation” to avoid\nmisrepresentation, as the cited references primarily report drought effects. The revised sentence\n(Line 355) now reads: [In Mongolian grasslands, persistent severe droughts have constrained\nvegetation growth43–45, which are regionally associated with elevated dust emissions (Fig. 2),\nthereby amplifying a positive feedback between vegetation decline and dust generation46.]\n[Comments] l. 351: ref 47 does not mention “restoration”.\n[Responses] Thank you for the comment. We have revised sentence (Line 358-360) now reads:\n[However, this negative trend can be mitigated through targeted human interventions47,48, such as\nthe \"Grain for Green\" project launched in 2000, which aimed to reduce dust emissions in\ndesertification-prone regions.]\n[Comments] Fig. 2e: I still do not quite understand how the authors interpret the trends on the\n2001-2015 period: as it cannot be at the same time a decreasing and increasing trend. I see there\nis a plateau, but then interpreting trends on Phase I and Phase II, each of which includes the\n2001-2015 period as a large part of their duration, weakens the interpreted trends.\n[Responses] Thank you for your comment. In response to your suggestion, we have removed the\n1982–2015 fitting line from Fig. 2e to ensure clearer representation of the distinct trends during\nPhase 1 (1982–2001) and Phase 2 (2001–2023).\n[\nphases. a and b, Trends in modeled dust time series from 1982 to 2015 (Phase 1) and from 2001\nto 2023 (Phase 2); diagonal lines denote trends significant at P < 0.05. c and d, Trends in dust\nemissions attributed to individual drivers (Experiment 1; Supplementary Table 1). e, Simulated\ndust emission trends from 1982–2023 under baseline (solid) and fixed-vegetation (dashed)\nconditions (Experiment 2). f, Difference between the two scenarios in (e), showing an increasing\nvegetation contribution after 2000. The dashed circle marks an outlier (2001) within 2001–2015;\nthe bold number in parentheses indicates the slope after its exclusion. Asterisks denote significant\ndifferences (* P < 0.05; *** P < 0.001).]\n[Comments] Fig. 5: this is a rather large figure, even larger as it is duplicated into two panels,\nonly differing by a “+” and two “-“.\n[Responses] Thank you for your comment. We have removed Fig. 5 from the manuscript. As\nmentioned in our previous responses, we have removed a series of content that was deemed\nperipheral to the core focus of the article.\n[Comments] Ref 13: Gillett -> Gillette.\n[Responses] Thank you for pointing that out. We have corrected the reference to \"Gillette\" as\n18\nsuggested.\n[Comments] l. 565: It would be interesting to mention in which years are added critical data (for\nDOD assessment) in the MERRA2 algorithm (e.g. SEVIRI in 2012, OMI in 2004, AMSU in 2002)\nand to discuss the impacts it may have on the outgoing DOD. Besides, you might want to have\na look at this recent publication on MERRA skills for DOD:\nZhu, Q., Liu, Y., & Xiao, N. (2025). The Frequency and Intensity of Extreme Dust Events and\nRelated Driving Factors in Major Dust Sources Based on MERRA-2 Aerosol Reanalysis. Journal\nof Climate.\n[Responses] Thank you for your suggestion. In response, we have added a discussion regarding\nthe critical data inputs used in the MERRA-2 algorithm for DOD assessment. We appreciate your\nrecommendation to consult the recent publication, and have integrated relevant insights from this\nstudy into our manuscript (Line 597–609).\n[Uncertainties in MERRA-2 DOD data stem from the evolving quality and availability of\nassimilated observational inputs over time. These include AVHRR (1980–2002), AERONET\n(1999–2014), MISR (2000–2014), MODIS Terra (2000 onward), and MODIS\nAqua (2002 onward)61. For example, MERRA-2 may show discrepancies in regions such as\nthe Taklamakan Desert, where sparse ground observations and regional differences in data\nassimilation reduce the accuracy of DOD estimates62. To address these potential biases, we\nincorporated independent ground-based observations to validate the dust emission simulations.\nSpecifically, we used springtime (March–May) dust storm day records from five WMO\nmeteorological stations downwind in northern China spanning 2001–2023 (locations shown in\nExtended Data Fig. 5a; metadata in Supplementary Tables 3 and 4). These records provide an\nobservational benchmark for assessing model performance beyond assimilated datasets.]\n[Comments] l. 573: You mean Ext. Data Fig 5?\n[Responses] Thank you for pointing that out. I made an error and mistakenly referred to Extended\nData Fig. 4 instead of Fig. 5. I have corrected the reference accordingly (Line 596).\n[Comments] l. 577: Why would MERRA2 DOD be less certain prior to 2001?\n[Responses] We have added an explanation (Line 614-615) regarding the uncertainties in\nMERRA-2 data prior to 2000, highlighting the limited availability of high-quality observational\ndata for assimilation during that period. [Moreover, although MERRA-2 data prior to 2000 may be\nless certain due to the limited availability of high-quality observational data for\nassimilation61, the high consistency observed between DuEM simulations using GIMMS and\nMODIS vegetation datasets during their overlapping period (2001–2015) (Extended Data Fig. 5c)\nsupports the credibility of the entire simulation period, including earlier years.]\n[Comments] l. 645: Are these monthly time-series?\n[Responses] Thank you for the valuable comment. We apologize for the confusion in the previous\n19\nversion. In our study, two MODIS NDVI products were used for different purposes. The monthly\nMOD13A3 dataset was applied in the statistical attribution analyses to reduce noise and\nimprove trend stability, whereas the 16-day MOD13C1 dataset was used to calculate\nphenological indicators (e.g., green-up date, GUD), since monthly data are too coarse to capture\nvegetation seasonality. This clarification has been added in the revised manuscript (Line 684-685).\nWe also cited studies supporting the reliability of using biweekly NDVI data to estimate temporal\nchanges in GUD and temperature sensitivity. We have removed Supplementary Fig. 21, and\nGIMMS-based GUD is no longer used in the analyses.\n[To assess the potential impact of vegetation seasonality on dust emissions, we derived green-\nup date (GUD) from the 16-day MODIS NDVI dataset (MOD13C1, 0.05° resolution). The use\nof biweekly NDVI data, as suggested in previous studies, does not bias the estimation of temporal\nchanges in GUD or temperature sensitivity76.]\n[Comments] l. 660: over which period do you use MODIS data?\n[Responses] Thank you for your comment. We have now clarified the period over which the\nMODIS data was used (Line 701-702). [To assess how land use changes influence vegetation and\ndust emissions, we used two long-term land cover datasets: MODIS (MCD12Q1, 500 m,\nCollection 6.1, 2001–2022) and ESA CCI (300 m, 1992–2020).]\n[Comments] l. 662: are these land cover types annual or perennial? Is there any tree/woody\ncover in these areas?\n[Responses] Thank you for your comment. We have clarified that the dominant land-cover types\nare mainly perennial vegetation. Forest and woody covers occur only in a very small area along\nthe eastern margins of the Eastern Sources (Line 705-706), and the role of perennial woody and\nsemi-woody shrubs has been noted in the Alashan Plateau ecosystem (Line 722-726).\n[Both datasets were reclassified into four broad biome types—barren, grasslands, croplands,\nand other—of which only the first three were used due to their dominance in dust source regions.\nForests were excluded from the analysis because they are limited to localized mountainous\nareas surrounding the dust source regions (Extended Data Fig. 6, gray-shaded region).]\n[To inform region-specific dust mitigation strategies, we further examined differences among\necoregions using the 'Terrestrial Ecoregions of the World' dataset79. This map, maintained by\nUNEP-WCMC, integrates biodiversity and conservation priorities across global ecoregions. We\nexcluded the Helanshan montane conifer forests, which contributed little to dust emission in\nEastern Sources, and selected five other ecoregions for analysis, namely, Alashan Plateau semi-\ndesert, Gobi Lakes Valley desert steppe, Eastern Gobi desert steppe, Mongolian-Manchurian\ngrassland, and Ordos Plateau steppe. Within these ecoregions, we quantified three potential\necological factors influencing vegetation’s role in dust emission. First, Water Limitation Index was\nassessed using the Pearson correlation between spring scPDSI and NDVI (MOD13A3) during\n2001–2023, where positive values indicate drought-constrained vegetation growth29. Second,\ngrazing pressure was represented by the change in total livestock (cattle, sheep, and goats)\nbetween 2020 and 2010 (FAO data: https://www.fao.org/livestock-systems/global-\ndistributions/en/) (Extended Data Fig. 7). Finally, dominant species and plant functional types\n20\nin the Alashan Plateau were examined using the 1:1,000,000 Vegetation Map of China,\nhighlighting the importance of perennial woody and semi-woody shrubs in shaping\nvegetation structure and influencing dust emissions from major source areas\n(Supplementary Table 2).]\n[Comments] l. 691: Is this the correlation with stations observations?\n[Responses] We thank the reviewer for the helpful comment. In our previous submission, ground-\nbased dust storm observations were not included for direct comparison between MERRA-2 and\nDuEM simulations. In the revised version, we have updated the original Supplementary Fig. 19 to\nincorporate these observational records. Although ground observations are valuable, their limited\nspatial coverage and temporal continuity pose challenges for long-term model validation.\nTherefore, we have retained the use of downwind DOD as an important indicator for evaluating\ndust emissions and associated dynamics (Lines 741–747).\n[The spatiotemporal reliabilities of the DuEM model have been verified using records of dust\nstorms, MERRA-2 DOD data and satellite images across hourly to annual scales (Extended Data\nFig. 5 and Supplementary Figs. 13–14). Compared with MERRA-2 outputs, DuEM dust emissions\nexhibited a finer spatial resolution of 0.25° × 0.25° and demonstrated higher simulation\nconfidence, as interannual correlations with downwind dust storm days increased from R2 =\n0.44 (MERRA-2) to R2 = 0.55 (DuEM) during 2001–2023 (Supplementary Fig. 14).]\n[\nb, Comparison of the spatial correlation between downwind DOD and dust emission from two\nsources: MERRA-2 (a) and DuEM (b). c, Validation of MERRA-2 DOD using observed dust\nstorm days, comparing DOD between the Eastern Sources and downwind dust-affected regions. d,\nScatterplots validating MERRA-2 dust emissions against dust storm days and downwind DOD. e\nand f, Same as in d, but for DuEM dust emissions, driven by vegetation products from GIMMS\n(Phase 1, 1982–2015; e) and MODIS (Phase 2, 2001–2023; f). The shaded lines indicate the\nconfidence intervals of linear fittings. *** indicates P < 0.001.]\n[Comments] l. 711: You mean Ext. Data. Fig. 5?\n[Responses] We appreciate the reviewer’s careful observation. The figure reference has been\ncorrected from Extended Data Fig. 4c to Extended Data Fig. 5c in the revised manuscript (Line\n747).\n[Comments] l. 747: to the dust -> to the interannual variability of spring dust emissions?\n[Responses] We thank the reviewer for the comment. We have revised the text (Line 803-804) to\nclarify the wording, changing “to the dust” to “to the interannual variability of spring dust\nemissions” as suggested. [In addition to the model coefficients, the PLSR model calculates\nvariable importance (VIP), with its absolute value representing the importance of the factor to the\ninterannual variability of spring dust emissions.]\n[Comments] l. 756-760: Do these products come from the ERA database?\n[Responses] We thank the reviewer for the comment. The climate indices were obtained from\nNOAA (CPC and NCEI; https://www.cpc.ncep.noaa.gov/ and\nhttps://www.ncei.noaa.gov/access/monitoring/products/) and calculated following standard NOAA\nprocedures. Detailed descriptions of each index and their computation methods are provided on\nthe NOAA websites, which are cited in the revised manuscript (Lines 811–818).\n[To explore these potential connections, we obtained multiple climate indices from National\nOceanic and Atmospheric Administration webpages (NOAA, https://www.cpc.ncep.noaa.gov/ and\nhttps://www.ncei.noaa.gov/access/monitoring/products/), including the Antarctic Oscillation\n(AAO), Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), El Niño-Southern\nOscillation (ENSO, using the Niño 3.4 index), North Atlantic Oscillation (NAO), Pacific Decadal\nOscillation (PDO), and the Pacific/North American (PNA) pattern, calculated following standard\nNOAA procedures.]\n[Comments] l. 771: vegetation density -> what do you mean? Vegetation density usually names\nvariables like number of plants per m². Do you rather mean vegetation cover (FCV)? Or aerial\nvegetation biomass (in kg per m²)?...\n[Responses] We thank the reviewer for the comment. We have revised the text to replace\n“vegetation density” with “vegetation cover” to avoid ambiguity (Line 829).\n[Comments] l. 798: About the way to take into account vegetation. In experiment 2, the effect of\nvegetation is held constant, but friction velocity u* varies over time. As u* is actually affected by\nvegetation cover, how do you deal with this?\n[Responses] We appreciate this insightful comment regarding the complex interaction between\nvegetation cover and friction velocity. We acknowledge that friction velocity (u*) is indeed\ninfluenced by vegetation cover, and it is crucial to account for this relationship in our experimental\ndesign. In Experiment 2, we aimed to isolate the effects of interannual and longer-term vegetation\nchanges on dust emissions. To achieve this, we held vegetation cover constant at its values from\nthe first year while still allowing for seasonal variations. This key distinction is important: u* was\ntherefore allowed to respond to the seasonal changes in vegetation structure, but not to the\ninterannual or longer-term changes. This approach enables us to quantify the specific contribution\nof interannual vegetation dynamics to dust emissions, independent of the influence of longer-term\nvegetation trends.\nIt should be mentioned that in this study, we isolated the direct effects from surface winds,\nvegetation cover, or soil moisture, individually. In fact, the interannual variations and long-term\nchanges of surface winds or soil moisture may partly result from those in vegetation cover, which\nis not separated. Therefore, it is likely that the overall impacts (including direct and indirect\neffects) of vegetation cover may be overestimated to some extent. We have clarified this in the\nmain text (Lines 407–413 and 856–864) and revised the notes of Supplementary Table 1\naccordingly.\n[This study acknowledges several uncertainties and limitations regarding data and model\nassumptions. First, surface winds, vegetation, and soil moisture are interdependent, and part\nof the interannual variability in wind or soil moisture may itself be vegetation‐driven23.\nAlthough controlled model experiments were designed to isolate the effects of individual\nfactors, they did not include explicit dynamic coupling among variables. Consequently,\nfeedbacks such as vegetation-induced modification of near-surface winds or soil moisture\npersistence could not be fully represented, leaving the net vegetation effect uncertain.\nSecond, this study primarily considers the effects of green vegetation, whereas the influence of dry\nor senescent vegetation—especially critical in regions where strong winds prevail outside the\ngrowing season—remains insufficiently constrained. Recent field surveys and satellite‐based\nanalyses have demonstrated that shortwave infrared (SWIR) indices, such as the MODIS Soil\nTillage Index (STI), can reliably estimate dry vegetation coverage and its control on surface\nroughness and threshold friction velocity in arid regions58,59. Incorporating such STI‐based\nestimates into future analyses would help quantify the mitigating role of dry vegetation in\ndust emission and narrow the current uncertainties associated with wind–vegetation\ninteractions59. A more detailed discussion of these and other methodological issues is provided in\nthe Supplementary Notes.]\n[Experiment 2: In this experiment, we isolated the effect of vegetation by holding it constant\nat its values from the first year, while allowing other variables—such as friction velocity, soil\nmoisture, and snow cover—to vary over time. Seasonal variations in vegetation were still\nincluded, so friction velocity could respond to seasonal vegetation structure, but interannual and\nlonger-term changes in vegetation were fixed. By comparing the results of this experiment with\nthe baseline, where all factors were allowed to vary, we were able to quantify the specific\ncontribution of vegetation to dust emissions. This approach strengthened the robustness of our\nresults by clearly distinguishing the role of vegetation from other influencing factors.]\n[Supplementary Table 1 | Experiments used in this study.\nExperiment name Simulation Surface wind vegetation Soil-\nperiods speed (Wind) cover (FVC) moisture\n(SoilM)\nBaseline DU All All All\nCOM\nExperiment 1 DU 1982–2015, All First First\nWind\nDU 2001–2023, First All First\nFVC\nDU 2015–2100 First First All\nSoilM\nExperiment 2 DU All First All\nWind+SoilM\nExperiment 3 DU All First First\nWind\nDU 2001–2017 First All First\nFVC\nDU First First All\nSoilM\nNote: The four experiments consist of Baseline, Experiment 1, Experiment 2 and Experiment 3.\n“DU” indicates the isolated contribution of dust changes caused by the corresponding factors\nrelative to the baseline (e.g., “DU ” is the baseline experiment, and “DU ” represents the\nCOM FVC\ncontribution of FVC interannual variations, with all other factors fixed at their first-year values).\n“All” and “First” refer to the input time series used in the DuEM model: “All” uses data from all\nyears, whereas “First” uses first-year data to fix interannual and longer-term variability\nwhile retaining seasonal variations.]\nReferences:\nWu, C., Lin, Z., Shao, Y., Liu, X. & Li, Y. Drivers of recent decline in dust activity over East Asia.\nNat. Commun. 13, 7105, doi:10.1038/s41467-022-34823-3 (2022).\n[Comments] Ext. Fig.. 9: In the legend of Fig. 2f, you mention that 2001 exhibits an\n“anomalously high value”. Thus, I wonder how this extremum translates into your conclusion\nabout trends, e.g. in the Ext Fig. 9 that compares 2010-2017 to 2001.\n[Responses] We thank the reviewer for the insightful comment. We understand the concern\nregarding the inclusion of 2001 in Extended Data Fig. 9 and its potential influence on the trend\nshown in Fig. 2f. We clarify that Extended Data Fig. 9 was based on Experiment 3 following\nWu et al. (2022), focusing on the 2001–2017 period when dust emissions showed a\npronounced decline. This period was selected as a targeted baseline experiment (see Fig. 3),\nusing MODIS-driven vegetation cover data to ensure accuracy and temporal consistency.\nIn contrast, Fig. 2f presents results from Experiment 2, highlighting the increasing\ncontribution of vegetation after 2000. In this figure, we have now explicitly shown the 2001\nvalue from the GIMMS NDVI3g-based simulation and marked it with a dashed circle to\nindicate its anomalous nature. The updated figure also provides the fitted slope for 2001–\n2015, with bold numbers in parentheses showing the slope after excluding this outlier. This\nclarification demonstrates that the anomalously high vegetation contribution in 2001 reduced the\nstatistical significance of the overall trend; after removing this value, the increasing trend becomes\nsignificant and aligns well with the MODIS-era simulation (2001–2023).\nAs noted in our previous responses, the former Extended Data Fig. 9 has been removed\ndue to partial redundancy. The updated Figs. 2 and 3 sufficiently capture the spatial and\ntemporal relationships between vegetation cover, its threshold-dependent effects, and the main\nfactors driving dust emission during the decline phase.\nWe have also revised the legend of Fig. 2f for clarity:\n[\nFig. 2 | The contributions of drivers to dust variations in Eastern Asia during two historical\nphases. a and b, Trends in modeled dust time series from 1982 to 2015 (Phase 1) and from 2001\nto 2023 (Phase 2); diagonal lines denote trends significant at P < 0.05. c and d, Trends in dust\nemissions attributed to individual drivers (Experiment 1; Supplementary Table 1). e, Simulated\ndust emission trends from 1982–2023 under baseline (solid) and fixed-vegetation (dashed)\nconditions (Experiment 2). f, Difference between the two scenarios in (e), showing an\nincreasing vegetation contribution after 2000. The dashed circle marks an outlier (2001)\nwithin 2001–2015; the bold number in parentheses indicates the slope after its exclusion.\nAsterisks denote significant differences (* P < 0.05; *** P < 0.001).]\nReferences\nWu, C., Lin, Z., Shao, Y., Liu, X. & Li, Y. Drivers of recent decline in dust activity over East Asia.\nNat. Commun. 13, 7105, doi:10.1038/s41467-022-34823-3 (2022).\n[Comments] Supplementary: l. 187: emissions -> GHG emissions (to avoid any confusion with\ndust emission)\n[Responses] We thank the reviewer for the helpful suggestion. We have revised the text (Line\n169) to specify “GHG emissions” instead of “emissions” to avoid confusion with dust emissions.\n[Comments] Supplementary: l. 295: 1 g/m² of what?\n26\n[Responses] We thank the Reviewer for catching this ambiguity. We have revised the text\n(Supplementary Line 273) to make this explicit. [+1 g m−2 per decade of dust emission\nreduction]\n[Comments] When mentioning NDVI or FCV (e.g. legend Suppl. Fig. 9 & 10), please specify if it\nis annual maximum of NDVI and of FCV.\n[Responses] We thank the reviewer for the helpful suggestion. As noted, the original\nSupplementary Fig. 9 has been removed, and the former Supplementary Figs. 10 and 11 have been\ncombined into the current Supplementary Fig. 7. We have clarified in the figure legend and\nmain text (Line 248-250) that FVC represents the multi-year mean of spring values, rather than\nannual maxima. This choice reflects the fact that dust activity peaks in spring, which is the season\nof interest in this study. Using annual maximum values could obscure the relationship between\nvegetation and dust emissions during this critical period.\n[Over 95% of dust emissions originate from regions where the spring fractional vegetation\ncover (FVC), averaged over 2001–2023 from MOD13C1, is below 15% (Fig. 3b).]\n[\nSupplementary Figure 7 | Relationships between vegetation cover (FVC) and soil properties.\na–c, Variations in soil texture (a, silt; b, sand; c, clay) along the spatial gradient of FVC,\nrepresented by the multi-year mean of springtime FVC (March–May) derived from MODIS\nMOD13C1, 2001–2023. d, Heatmap showing the distribution of soil organic carbon content\nacross gradients of FVC and soil sand fraction. The dashed lines represent the inflection points of\nsoil textures along with FVC detected by the Change Point Analysis v1.20 tool in OriginPro\n27\n2024.]\n[Comments] Suppl. Table 4: It would be interesting to have the period covered by each dataset,\nalong with their temporal resolution.\n[Responses] We thank the reviewer for the helpful suggestion. We have added the period covered\nby each dataset and their temporal resolutions in Supplementary Table 5 as suggested.\n[Supplementary Table 5 | List of variables included in statistical analyses.\nNote: * is used for random forests to account for the spatial pattern of MERRA-2 dust emissions during 2001–\n2020. The specific data processing methodology is detailed in the Methods section.\nCategory Variable Original Original Period used References\nspatial temporal in this study\nresolution resolution\nClimate Wind speed* 0.1° monthly 1982–2023 ERA5-Land (ref. 20)1\nClimate Wind direction* 0.1° monthly 2001–2020 ERA5-Land (ref. 20)1\nClimate Air 0.1° monthly 1982–2023 ERA5-Land (ref. 20)1\ntemperature*\nClimate Rainfall* 0.1° monthly 2001–2020 ERA5-Land (ref. 20)1\nClimate Net solar 0.1° monthly 2001–2020 ERA5-Land (ref. 20)1\nradiation*\nClimate Soil 0.1° monthly 2001–2020 ERA5-Land (ref. 20)1\ntemperature*\nClimate Soil moisture* 0.1° monthly 1982–2023 ERA5-Land (ref. 20)1\nClimate Precipitation 0.1° monthly 1982–2023 ERA5-Land (ref. 20)1\nClimate Snow-cover 0.05° daily 1982–2023 Calculated from AVHRR (ref. 21)2\nduration* and MOD10C1 (ref. 22)3\nClimate Aridity index* 1km — — Global Aridity Index and Potential\nEvapo-Transpiration (ET0) Database\nv3 (ref. 23)4\nClimate self-calibrating 0.5° monthly 2001–2023 Calculated from CRU TS4.06 (ref.\nPDSI (scPDSI)* 24)5\nVegetation MODIS NDVI* 1km monthly 2001–2023 MOD13A3 (ref. 29)3\nVegetation MODIS FVC 0.05° 16-day 2001–2023 Calculated from MOD13C1 NDVI\n(ref. 30)3\nVegetation MODIS green- 0.05° 16-day 2001–2020 Calculated from MOD13C1 NDVI\nup date* (ref. 30)3\nVegetation GIMMS 1/12° biweekly 1982–2015 Pinzon et al. (ref. 26); Tucker et al.\nNDVI3g (ref. 27)6\nVegetation PKU GIMMS 1/12° biweekly 1982–2015 Li et al. (ref. 28)7\nNDVI\nVegetation GIMMS LAI4g 1/12° biweekly 1982–2015 Cao et al. (ref. 25)8\nVegetation GLASS LAI* 0.05° biweekly 1982–2020 Liang et al. (ref. 30)9\n/ 1 km\nTopography Surface 1.8km — — Calculated from ETOPO-1 DEM by\nundulation* Arcmap 10.810\nTopography Surface aspect* 1.8km — — Calculated from ETOPO-1 DEM by\nArcmap 10.810\nTopography Slope* 1.8km — — Calculated from ETOPO-1 DEM by\nArcmap 10.810\nLand use 2010 IPCC 300m yearly 1992–2020 ESA CCI (ref. 31)11\nclassification*\nLand use IGBP 500m yearly 2001–2022 MCD12Q1 (ref. 32)3\nDust MERRA-2 dust 0.5 × monthly 1982–2023 MERRA-2 tavgM_2d_adg_Nx (ref.\nemissions 0.625° 33, 34)12\nDust MERRA-2 dust 0.5 × daily 1982–2023 MERRA-2 tavgM_2d_adg_Nx\noptical depth 0.625° / monthly (monthly) (ref. 33, 34)12 /\nMERRA-2 tavg1_2d_aer_Nx (daily,\nused for annual peak of dust\nactivity) (ref. 33, 34)12\nDust site-based dust — — 28 2001–2017 Based on 339 stations in Eastern\nstorms Sources\n(Wu et al.) (ref. 1)\nDust site-based dust — — 2001–2023 Based on 5 stations in dust-affected\nstorms region (Supplementary Tables 3 and\n4)\n1 https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview\n2 https://catalogue.ceda.ac.uk/uuid/3f034f4a08854eb59d58e1fa92d207b6?jump=external-citation-anchor\n3 https://search.earthdata.nasa.gov/\n4\nhttps://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Data\nbase_v2/7504448/5\n5 https://crudata.uea.ac.uk/cru/data/drought/\n6 https://poles.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88/\n7 https://zenodo.org/records/8253971\n8 https://doi.org/10.5281/zenodo.7649107\n9 http://www.glass.umd.edu/Download.html\n10 https://ngdc.noaa.gov/mgg/global/global.html\n11 https://maps.elie.ucl.ac.be/CCI/viewer/\n12 https://disc.sci.gsfc.nasa.gov/datasets/]\n[Comments] The importance of dry vegetation cover should not be underestimated. It is actually\ncrucial in areas where wind erosivity occurs out of the green vegetation cycle, which is at least\npartly the case in your study area, as illustrated by your Extended Data figure 2. In particular, you\ndo not mention remote sensing indices (like the STI: Soil Tillage Index) that could quite\neasily allow you to assess dry vegetation cover and refine your investigation on its impact on dust\nemission.\n[Responses] We thank the reviewer for emphasizing the role of dry vegetation. In Eastern Asia’s\nspring dust sources during our study period, dry vegetation can indeed affect dust emissions. Our\nmodel accounts for vegetation dynamics using indicators such as FVC and NDVI, which\nindirectly capture the spatial and interannual variability of dry vegetation: regions with higher\nFVC generally coincide with greater dry vegetation, and their interannual changes are largely\ncomplementary. We acknowledge that explicitly integrating dry vegetation indices would\nfurther improve the model constraints on dust emissions (Wu et al., 2001, 2023). We have\ntherefore added a relevant discussion to the main text (Lines 413–422) and a corresponding\nnote in the Supplementary Information (Lines 252–263), along with the inclusion of new\nreferences (now cited as refs. 58 and 59).\n[This study acknowledges several uncertainties and limitations regarding data and model\nassumptions. First, surface winds, vegetation, and soil moisture are interdependent, and part\nof the interannual variability in wind or soil moisture may itself be vegetation‐driven23.\nAlthough controlled model experiments were designed to isolate the effects of individual\nfactors, they did not include explicit dynamic coupling among variables. Consequently,\nfeedbacks such as vegetation-induced modification of near-surface winds or soil moisture\npersistence could not be fully represented, leaving the net vegetation effect uncertain.\nSecond, this study primarily considers the effects of green vegetation, whereas the influence of dry\nor senescent vegetation—especially critical in regions where strong winds prevail outside the\ngrowing season—remains insufficiently constrained. Recent field surveys and satellite‐based\nanalyses have demonstrated that shortwave infrared (SWIR) indices, such as the MODIS Soil\nTillage Index (STI), can reliably estimate dry vegetation coverage and its control on surface\nroughness and threshold friction velocity in arid regions58,59. Incorporating such STI‐based\nestimates into future analyses would help quantify the mitigating role of dry vegetation in\ndust emission and narrow the current uncertainties associated with wind–vegetation\ninteractions59. A more detailed discussion of these and other methodological issues is provided in\nthe Supplementary Notes.]\n[The vegetation indices used (e.g., FVC and NDVI) primarily capture photosynthetically\nactive biomass and do not directly account for non-green components such as litter or residues,\nwhich also help protect the soil. Dry vegetation contributes to surface stabilization, and regions\nwith higher FVC may coincide with areas containing more dry biomass, although interannual\nvariations in FVC do not necessarily track those of dry vegetation. Consequently, our model\npartially captures the protective effect of dry vegetation on dust emissions. Process-based\nsimulations further support this: when interannual or long-term vegetation changes were included\nin controlled experiments, dust emission trends shifted from positive to negative, demonstrating a\ncausal suppressive effect. Future studies could refine this assessment by incorporating direct\nmeasurements of dry biomass or remote sensing products targeting senescent vegetation.]\nReferences:\nWu, J. et al. Estimation of dry vegetation cover and mass from MODIS data: verification by\nroughness length and sand saltation threshold. Int. J. Appl. Earth Obs. Geoinf. 102, 102417,\ndoi:10.1016/j.jag.2021.102417 (2021).\nWu, J., Kurosaki, Y., Sekiyama, T. T. & Maki, T. Effects of dry vegetation coverage estimated\nfrom the MODIS Soil Tillage Index on dust occurrence: verification by surface synoptic\nobservations. J. Meteorol. Soc. Jpn. Ser. II 101, 67–77, doi:10.2151/jmsj.2023-004 (2023).\n[Comments] I do not understand this sentence: “As shown in Extended Data Fig. 5c, dust\nemissions simulated during Phase 2 (MODIS-based, 2001–2023) exhibit higher correlation with\nMERRA-2 DOD (R2= 0.67) compared to Phase 1 (GIMMS-based, 1982–2015; R2= 0.53),\nsuggesting improved MERRA-2 data quality after 2001 due to better observational constraints.”\nDo you mean that the better agreement of simulations with MERRA proves that MERRA quality is\ngood? That would imply that the simulations are the reference, whereas your article is the other\nway around: MERRA is taken as the reference, to which the simulations are compared in order to\nassess their quality.\n[Responses] We thank the reviewer for this comment. In our previous response, we attempted to\naddress the concern that the potential limitations of the MERRA-2 DOD should be explicitly\ndiscussed, but our explanation may not have been sufficiently clear. In this revision, we have\nadded a dedicated paragraph in the Methods section (Lines 597–603) discussing the temporal\nconsistency and potential biases of MERRA-2 associated with evolving observational inputs. We\nappreciate the reviewer’s suggestion, which has helped us to improve this part of the manuscript.\n[Uncertainties in MERRA-2 DOD data stem from the evolving quality and availability\nof assimilated observational inputs over time. These include AVHRR (1980–2002),\nAERONET (1999–2014), MISR (2000–2014), MODIS Terra (2000 onward), and MODIS\nAqua (2002 onward)61. For example, MERRA-2 may show discrepancies in regions such as\nthe Taklamakan Desert, where sparse ground observations and regional differences in data\nassimilation reduce the accuracy of DOD estimates62. To address these potential biases, we\nincorporated independent ground-based observations to validate the dust emission simulations.\nSpecifically, we used springtime (March–May) dust storm day records from five WMO\nmeteorological stations downwind in northern China spanning 2001–2023 (locations shown in\nExtended Data Fig. 5a; metadata in Supplementary Tables 3 and 4). These records provide an\nobservational benchmark for assessing model performance beyond assimilated datasets.]\n[Comments] Code availability: The code is now accessible and clear, yet the authors might\nconsider adding a “readme” file to explain the structure of the files and sub-files, and to detail\nwhere can be found the different parts of the code of the dust emission model.\n[Responses] We thank the reviewer for this helpful suggestion. We have now added a detailed\nREADME file explaining the structure of the folders and sub-folders, and indicating where each\npart of the dust emission model code can be found\n(https://figshare.com/s/fe3183386df90ca6d7d6).\nWe thank Reviewer #2 for their co-review in collaboration with a primary reviewer,\nand we appreciate Nature Communications’ initiative to support Early Career\nResearchers through this process.\n[Comments] I acknowledge again the work done by the authors to take into account my comments.\nI still have some comments, most of which are minor.\nConcerning my not-so-minor comments, I will leave it to the editor or other referees, as I do not\nwant to obstruct the handling procedure of this manuscript. I will specify it IN CAPITAL below..\n[Responses] Thanks very much to the reviewer for their careful evaluation and constructive\ncomments. All points have been addressed in the responses below.\n[Comments] About the importance of vegetation type: the perennial and woody nature of the\nvegetation which actually inhibits wind erosion in their study area is now specified in several places\nof the manuscript. However, it is not the case in the title, which only mentions “Vegetation\ngreening”, as well as the abstract. To avoid any confusion, some precisions on the vegetation type\nshould be given, at least in the abstract.\n[Responses] We thank the reviewer for highlighting the potential ambiguity regarding vegetation\ntype. In response, we have revised the Abstract (Line 37) to explicitly specify that the dust\nmitigation effect is primarily associated with perennial dryland vegetation in sparsely vegetated\nsource regions, thereby clarifying the nature of the vegetation responsible for reducing wind erosion.\nWe retain the more general term “vegetation greening” in the title to preserve broader\napplicability, while ensuring that the Abstract and main text now clearly define the relevant\nvegetation characteristics and mechanisms.\n[Comments] The number of figures: there is still a large number of figures in the manuscript (main\n+ extended + supplementary) and a huge number of panels altogether (about 190). I LEAVE IT TO\nTHE EDITOR’S JUDGEMENT. About the framing of the figures: I still find that most maps could\nbe reframed, as they are illegible as is, and as the reframing wouldn’t prevent understanding of\nthe analysis. I LEAVE IT TO THE EDITOR’S JUDGEMENT.\n[Responses] We thank the reviewer for the comment. We have re-examined all figures and revised\ncharacter sizes and layouts to improve readability and clarity, particularly for map-based figures.\nWhere feasible, framing was adjusted without affecting the analyses.\n[Comments] Definition of the time periods: I still do not quite understand how the authors interpret\nthe trends on the 2001-2015 period in Fig 2e: as it cannot be at the same time a decreasing and\nincreasing trend. The authors removed the line in the figure but did not change their interpretation\n(e.g. l. 183). My interpretation of this figure is an increasing trend up to 2000 or 2005, not to\n2015 as stated in the manuscript. Idem l. 191: the authors cannot at the same time state that there is an\nincrease until 2015 and a decrease from 2001. In addition, MERRA algorithm shows 2 important\ndates with changes: around 2000-2002 (end of AVHRR, start of MODIS and MISR), and 2014 (end\nof AERONET and MISR), which correspond exactly to the trends analyzed by the authors. I LEAVE\nIT TO THE EDITOR’S JUDGEMENT.\nP\nA\nG\nE\n[Responses] We thank the reviewer for pointing out the ambiguity in our previous interpretation of\ntrends during the 2001–2015 period. We agree that this interval should not be interpreted as\nsimultaneously exhibiting increasing and decreasing trends.\nIn response, we have redefined the temporal framework and revised the text and figures\naccordingly. Specifically, trend analyses are now conducted for two periods, 1982–2000 and 2001–\n2023. The overlapping interval (2001–2015) is no longer treated as a monotonic trend, but is used\nsolely as a high-emission transition/plateau period for cross-validation between the GIMMS- and\nMODIS-driven simulations, without implying an independent trend.\nWe have revised the corresponding descriptions in the Results section (Lines 156), updated\nFig. 2 and its caption, and ensured that Supplementary Fig. 9 follows the same temporal\ndefinitions. These revisions also account for major observational and algorithmic transitions around\n2000–2002 and 2014.\nAlthough the temporal segmentation has been refined, the underlying simulations remain\nunchanged and the main conclusions of the study are unaffected.\n[Comments] Grain for Green project: this project is mentioned l. 359 without any explanation\nabout its context and its effective success. Besides, what is the overlap between the Grain for Green\nproject and the dust emission source areas identified in this study?\n[Responses] We thank the reviewer for this helpful comment. We have clarified the context of the\nGrain for Green program in the Discussion by briefly introducing it as a large-scale ecological\nrestoration initiative launched around 2000 to restore perennial vegetation in desertification-prone\nregions of northern China. We further cited previous studies demonstrating that such restoration\nefforts can effectively reduce wind erosion and associated dust emissions.\nWe note that our study does not aim to quantify the spatial overlap or isolate the direct impact\nof specific restoration programs on dust emission sources. The reference to Grain for Green is\ntherefore intended to provide contextual support for the role of human interventions in modulating\nvegetation–dust feedbacks, rather than to attribute the observed dust changes to a particular policy.\nWe have revised the text accordingly to avoid overinterpretation (Lines 307–311).\n[However, this negative trend can be mitigated through targeted human interventions47,\nsuch as China’s Grain for Green program, initiated around 2000 to restore perennial\nvegetation in desertification-prone regions of northern China that overlap with parts of\nthe dust source areas considered in this study, which previous studies suggest can\nreduce wind erosion and associated dust emissions48.]\nMinor comments:\n[Comments] l. 41: you can keep “priority areas for ecological restoration”.\n[Responses] Done.\n[Comments] l. 56: “interactions with wind-driven processes”: unclear.\n[Responses] We have clarified this phrasing by specifying that wind acts primarily as a trigger for\ndust emissions from surfaces whose susceptibility is modulated by vegetation. The revised sentence\nP\nA\nG\nE\n(Line 53–56) now reads: [Substantial vegetation changes have been observed in drylands12,\nbut their effects on dust activity remain highly heterogeneous because wind merely\ntriggers emissions from surfaces whose susceptibility is modulated by vegetation13–15]\n[Comments] l. 58: “dust emissions variability”, not just “dust variability”.\n[Responses] Done.\n[Comments] l. 76: same as l. 58: “dust emission dynamics”, not just “dust dynamics”\n[Responses] Done.\n[Comments] l. 116: I do not quite understand this sentence, as Suppl. Figure 3i actually show wind\neffect almost everywhere.\n[Responses] We thank the reviewer for the comment. Supplementary Fig. 3 (now Supplementary\nFig. 6) was simplified to focus on the Eastern Sources and shows only regions with statistically\nsignificant correlations between wind speed and dust emissions. In this context, the text emphasizes\nthat wind effects are particularly pronounced in sparsely vegetated and low-soil-moisture areas,\nwhile regions with moderate vegetation or soil moisture exhibit weaker or non-significant\ncorrelations. We clarify that this description is consistent with the simplified figure, and no changes\nto the text were made.\n[Comments] l. 123: do you mean “ > 0.18 “ (instead of “ < 0.18 “)?\n[Responses] Thank you for pointing this out. We have corrected “< 0.18” to “> 0.18” in the revised\nmanuscript.\n[Comments] l. 186: In my opinion Figure 4 mostly shows a decrease in vegetation cover.\n[Responses] We thank the reviewer for the comment. After redefining the time periods, we\nspecifically calculated the areas of vegetation greening and browning and their respective\ncontributions to dust emissions. This analysis now shows that during 1982–2000, vegetation\nbrowning dominated over greening in the Eastern Sources, leading to a small net increase in dust\nemissions, consistent with the revised temporal framework.\n[Comments] l. 207: 29.5% where does this value come from?\n[Responses] We thank the reviewer for pointing this out. The −29.5% value refers to the relative\ncontribution of vegetation to dust emission reductions during 2001–2023, calculated from\nExperiment 2. Specifically, we updated the calculation using the more robust Theil–Sen slopes:\ndust emissions decreased from −0.25 g m−2 yr−1 under fixed-initial-year vegetation to −0.37\ng m−2 yr−1 with time-varying vegetation, indicating that vegetation-driven reductions account for\nroughly 32.5% of the total decline. The annual difference shows a significant increase over the\nP\nA\nG\nE\nperiod (~0.1 g m−2 per year), consistent with the intensifying greening trend in Eastern Sources\n(Supplementary Fig. 9e) and is now also indicated in Fig. 2e, f.\n[Comments] l. 212-215: Actually, we can see opposite trends already since 1980, with increasing\nvegetation cover and decreasing precipitation and soil moisture.\n[Responses] We thank the reviewer for this comment. We clarify that the strengthened linkage\nbetween vegetation and DOD reductions after 2000 (Supplementary Fig. 11b) is based on the rolling\ncorrelation analysis, whereas the observed decoupling of vegetation from soil moisture and\nprecipitation (Supplementary Fig. 11a) is an independent finding highlighting the potential influence\nof other factors, such as CO fertilization or ecological restoration. The text has been revised to\n2\nmake this distinction clearer (Lines 184–189).\n[Comments] l. 216-217: vegetation increases mostly for cropland and grassland, not that much\nfor barren land?\n[Responses] We agree with the reviewer that vegetation increases are larger in grassland and\ncropland than in barren land. We therefore revised the text (Line 191) to explicitly report the\nmagnitude of vegetation change for each land cover type. Vegetation cover increased significantly\n(P < 0.001) from 2001 to 2023, with mean FVC increases of ~0.3 per decade in barren land, ~1.4\nper decade in grassland, and ~1.7 per decade in cropland (Supplementary Fig. 12).\n[Comments] l. 218: is this a net area (deducing the area from other land cover types that was\ntransformed to barren land)?\n[Responses] This estimate represents the gross area of barren land converted to grassland and does\nnot subtract reverse transitions. We have clarified this in the revised text (Line 194).\n[Comments] l. 230: annual or perennial vegetation?\n[Responses] The remaining four ecoregions are dominated by perennial dry steppe and semi-arid\ngrassland vegetation characteristic of the Mongolian Plateau. We have clarified this in the revised\nmanuscript.\n[Comments] l. 238-239: where does the values 80.7% and 2.11% come from?\n[Responses] These values are obtained from Experiment 3 and quantify the relative contribution of\neach driver to dust emission changes relative to the 2001 baseline. This has been clarified in the\nrevised manuscript.\n[Comments] l. 254: reflect -> imply?\n[Responses] We have replaced “reflect” with “imply” as suggested to improve semantic precision.\nP\nA\nG\nE\n[Comments] l. 286: perennial/woody vegetation? Idem l. 288\n[Responses] The vegetation referred to here is primarily perennial vegetation within dust source\nregions. We have clarified this in the revised text.\n[Comments] l. 307-309: this sentence is confusing. Do you mean that future scenarios will account\nfor land-use optimization?\n[Responses] We have clarified that land-use optimization refers to drivers of past greening, whereas\nthe projected continuation of greening is based on future climate scenario simulations. The sentence\nhas been revised to avoid confusion (Lines 268–271).\n[Comments] l. 330: this depends on the objective of the study.\n[Responses] We have revised the text (Line 278) to rephrase this statement as a key finding of our\nanalysis, emphasizing the role of long-term trajectories rather than short-term fluctuations.\n[Comments] l. 332: “no consistent trend” I think that you actually have shown an increasing\ntrend in dust emission followed by a decreasing trend?\n[Responses] We agree and have revised the text accordingly. The sentence now reads: [Over the\npast four decades, dust emissions have exhibited a non-monotonic trend, characterized by an initial\nincrease followed by a subsequent decline, driven by the combined influences of vegetation, wind\nspeed, and soil moisture.]\n[Comments] l. 335: perennial/woody vegetation\n[Responses] We have clarified that the greening referred to here is primarily associated with\nperennial woody and semi-woody vegetation in key dust source regions.\n[Comments] l. 634: Do you mean that there has been a change in land cover type?\n[Responses] We clarify that the greening discussed here primarily reflects increases in vegetation\ncover and productivity within existing land cover types, rather than a widespread conversion\nbetween major land cover classes, unless explicitly stated otherwise.\n[Comments] l. 379-380: to reduce “dust emissions” or “dust emission impacts” ?...\n[Responses] We have clarified that the principles are aimed at reducing dust emissions and their\npotential impacts, and revised the wording accordingly.\n[Comments] l. 390: a few words on the importance of vegetation type could be useful.\n[Responses] We have added a brief clarification noting that the effectiveness and feasibility of the\nP\nA\nG\nE\n15% vegetation threshold depend on vegetation type (Line 343).\n[Comments] l. 431: same as for l. 390\n[Responses] We have clarified that the long-term benefits of vegetation greening are primarily\nassociated with perennial dryland vegetation in dust source regions.\n[Comments] l. 432: this actually depends also on the wind speed.\n[Responses] We agree and have clarified that the long-term benefits of vegetation greening are\nmodulated by changes in wind speed.\n[Comments] l. 605: WMO data were not included in MERRA?\n[Responses] No. Dust storm day records from WMO meteorological stations are not assimilated\ninto the MERRA-2 aerosol reanalysis. We have clarified this point in the revised text (Line 560).\n[Comments] l. 614-618: I do not understand the reasoning here. This would only suggest that the\nsimulations sensitivity to vegetation is low, or that GIMMS and MODIS product are in good\nagreement, but I do not understand how it demonstrates that the simulations are reliable.\n[Responses] We agree and have revised the text to avoid overinterpreting the GIMMS–MODIS\nconsistency as a validation of early-period simulations (Line 570).\n[Comments] l. 637: please briefly specified why you excluded the North China Plain.\n[Responses] We have clarified that the North China Plain was excluded because it primarily\nfunctions as a dust receptor region, with local emissions much smaller than those from the upwind\narid and semi-arid source areas (Line 592).\n[Comments] l. 699: I think there is a confusion here between land use and land cover type?\n[Responses] We have revised the text to clarify the distinction between land use and land cover, and\nnow refer to land-use–driven changes in land cover, consistent with the datasets used (Line 651).\n[Comments] l. 719-720: Could you please explain your choice? Was it because of data availability?\n[Responses] We have clarified that changes in total livestock were used because gridded FAO data\nare available and provide a consistent, spatially comparable proxy for grazing intensity across\necoregions.\nP\nA\nG\nE\nPoint-by-point response to comments of Reviewer 2\n[Comments] I co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\n[Responses] We thank Reviewer #2 for their co-review in collaboration with a primary reviewer,\nand we appreciate Nature Communications’ initiative to support Early Career Researchers through\nthis process.\nP\nA\nG\nE"}]

Decision letter