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American Association for the Advancement of Science

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Atmospheric Science
Decreased dust particles amplify the cloud cooling effect by regulating cloud ice formation over the Tibetan Plateau
Decreased dust amplifies cloud cooling effect
https://orcid.org/0000-0002-5200-982X
Chen Jingchuan Conceptualization Data curation Formal analysis Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing - original draft Writing - review & editing 1
Xu Jianzhong Conceptualization Investigation Methodology Supervision Writing - review & editing 2 3
https://orcid.org/0000-0002-7705-2373
Wu Zhijun Conceptualization Funding acquisition Project administration Resources Supervision Writing - review & editing 1 4 *
https://orcid.org/0009-0005-9655-5119
Meng Xiangxinyue Conceptualization Investigation Validation Writing - review & editing 1
https://orcid.org/0000-0003-2233-344X
Yu Yan Formal analysis Methodology Software Validation Visualization Writing - review & editing 5
https://orcid.org/0000-0003-3642-2988
Ginoux Paul Resources Writing - review & editing 6
https://orcid.org/0000-0002-3719-1889
DeMott Paul J. Conceptualization Validation Writing - review & editing 7
Xu Rui Formal analysis Investigation Software 5
Zhai Lixiang Data curation Investigation Resources 3
Yan Yafei Investigation 8
https://orcid.org/0000-0002-5196-3996
Zhao Chuanfeng Writing - review & editing 5
https://orcid.org/0000-0002-7628-6581
Li Shao-Meng Conceptualization Supervision Writing - review & editing 1
https://orcid.org/0000-0002-2752-7924
Zhu Tong Writing - review & editing 1
https://orcid.org/0000-0003-4816-9123
Hu Min Writing - review & editing 1
1 State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
2 School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China.
3 State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
4 Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
5 Department of Atmospheric and Oceanic Sciences, School of Physics, Institute of Carbon Neutrality, Peking University, Beijing 100871, China.
6 Geophysical Fluid Dynamics Laboratory, NOAA/OAR, Princeton, NJ, USA.
7 Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA.
8 School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China.
* Corresponding author. Email: zhijunwu@pku.edu.cn
13 9 2024
13 9 2024
10 37 eado088516 1 2024
09 8 2024
Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
2024
The Authors
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Ice-nucleating particles (INPs) can initiate cloud ice formation, influencing cloud radiative effects (CRE) and climate. However, the knowledge of INP sources, concentrations, and their impact on CRE over the Tibetan Plateau (TP)—a highly climate-sensitive region—remains largely hypothetical. Here, we integrated data from multisource satellite observations and snowpack samples collected from five glaciers to demonstrate that dust particles constitute primary INP sources over the TP. The springtime dust influxes lead to seasonally elevated ice concentrations in mixed-phase clouds. Furthermore, the decadal reduction in dustiness from 2007 to 2019 results in decreased springtime dust INPs, thereby amplifying the cooling effect of clouds over the TP, with a 1.98 ± 0.39–watt per square meter reduction in surface net CRE corresponding to a 0.01 decrease in dust optical depth. Our findings elucidate previously unidentified pathways of climate feedback from an atmospheric INP perspective, especially highlighting the crucial role of dust in aerosol-cloud interactions.

The impacts of dust particles as INPs on cloud radiative effects in Tibetan Plateau aerosol-cloud interactions are presented.

National Natural Science Foundation of China Creative Research Group Fund 22221004 National Natural Science Foundation of China and Swedish Foundation for International Cooperation 42011530121
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pmcINTRODUCTION

Atmospheric ice-nucleating particles (INPs) are pivotal in cloud ice formation processes [e.g., (1–3)], thereby profoundly influencing cloud radiative effects (CRE) through alterations in the physical and optical properties of clouds, as well as affecting the Earth’s radiation budget [e.g., (4–7)]. The polar regions, known for their acute sensitivity to climate change, underscore the vital role clouds play in polar amplification [e.g., (8, 9)]. Accurate assessments of radiative forcing in these regions are impeded by the complexities inherent in modeling mixed-phase clouds, which exhibit high sensitivity to INPs (10, 11). Notably, the Tibetan Plateau (TP), also referred to as the Third Pole, is undergoing a warming trend at a rate twice the global average observed in the past five decades, a phenomenon where clouds are likely contributing factors to this rapid warming (12, 13). Known as the Water Tower of Asia, the TP is vital for the water supplies of billions of people in downstream East and South Asia, and it influences climate change across East Asia and the Pacific through the vertical exchange of water and heat (14, 15). This underlines the critical importance of climate change research on the TP. Despite its significance, the role of INPs in aerosol-cloud interactions over the TP has been frequently overlooked in prior research, primarily due to a dearth of observational data (15). The sources, concentrations, and geographical variability of INPs in this region remain largely underexplored. Therefore, investigating INPs is essential for a more profound comprehension of cloud processes and radiative effects in this climatically sensitive area and for enhancing the accuracy of models in portraying current conditions and predicting future climatic shifts (11).

Among the various identified INPs sources, mineral and soil dust particles are recognized globally as particularly important, owing to their effective ice nucleation activity (INA), abundant presence in the atmosphere, and substantial capacity for intercontinental, and even global, transport [e.g., (16–21)]. Extensive observations and simulations of INPs conducted in diverse regions, including the Arctic (22–28), North America (29, 30), Europe (31, 32), and the Southern Ocean (33–35), have demonstrated that both long-range transported and locally sourced dust particles can efficiently serve as seeds under conditions relevant for mixed-phase cloud, regulating cloud ice formation, precipitation, and radiation budgets [e.g., (36–38)]. Compared to the Arctic and Antarctic regions, the TP exhibits a notably higher frequency of dust-related particle occurrence (39). However, current research on dust particles, cloud macroscopic and microscopic properties, and radiative forcing in this area still lacks cohesive integration, primarily focusing on statistical analyses and case studies (40, 41). In particular, our understanding of INPs in the TP region is still in its infancy (42, 43), with considerable uncertainties surrounding their abundance, sources, and potential climatic impacts, posing formidable challenges in constraining cloud and climate in models.

To fill these knowledge gaps, this study aims to: (i) elucidate the mechanisms driving seasonally elevated cloud ice number concentrations over the TP, using satellite-based and reanalysis data; (ii) determine the concentration, spatial distribution, and sources of INPs over the TP through field observations; and (iii) explore the long-term trend of springtime CRE and their response to potential dust INPs in the TP region. Here, we report on the immersion mode INP properties of snowpack samples collected from five glaciers throughout the TP region, identify the dominant factors affecting INP concentrations via multiple lines of experimental evidence, and discuss their implications for cloud ice number concentrations and radiative effects.

RESULTS

Dust INPs regulate cloud ice formation over the TP in spring

Satellite observations obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), spanning the period from 2006 to 2019, revealed that the multiyear monthly cloud fraction within the entire TP region [defined as above 2500 m above mean sea level (AMSL) in this study, fig. S1; Materials and Methods] fluctuated from 49.8 to 64.6% (fig. S2 and table S1). During the spring months from March to May, the cloud fraction reached its seasonal maximum, exceeding 60% (fig. S3 and table S2). In addition, the cloud top height, ranging from 7.5 to 8.2 km AMSL, coupled with corresponding temperatures of about −25.0° to −29.2°C, was conducive to the formation and development of mixed-phase clouds. The high cloud fraction, lower cloud base height (around 2 km above ground), and suitable temperature conditions suggested the potential for extensive and intense aerosol-cloud interactions over the TP during spring, with a specific emphasis on mixed-phase clouds.

As an example of how dust particles participate in cloud formation during the spring season (Fig. 1, A to C), observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) have elucidated that a substantial amount of dust was uplifted to altitudes above the 0°C isotherm, especially above the −20°C isotherm, coming into direct interaction with clouds. These potential aerosol-cloud interaction events, where dust particles and clouds are simultaneously observed within the altitude range corresponding to temperatures from −10° to −40°C, are authenticated through statistical analysis of CALIPSO daily data during the spring months of 2008, 2009, 2017, and 2018. Over the TP region, more than half of the dust events involved these potential aerosol-cloud interaction events (averaging 50.2%; Fig. 1, D and E). This highlights the considerable potential of these dust particles to act as INPs, actively contributing to the formation of mixed-phase clouds and ice clouds over the TP.

Fig. 1. Aerosol vertical distribution and aerosol-cloud interactions over the TP in spring.

(A) The orbit track of the CALIPSO on 12 May 2010 is shown. The satellite track is indicated by the column optical depth of tropospheric aerosols (tropospheric AOD) at 532 nm. (B) The vertical distributions of dust particles (including polluted dust, shown in gold) and clouds (shown in sky blue) along the CALIPSO track. Dust and polluted dust are classified on the basis of the volume depolarization ratio for features identified as aerosols, while clouds are identified as distinct features in the CALIPSO product. (C) The vertical distributions of the aerosol total backscatter coefficient at 532 nm. Spatial (D) and latitudinal (E) probability statistics of potential aerosol-cloud interaction events over the TP during the spring months of March to May for the years 2008, 2009, 2017, and 2018. In the CALIPSO daily data (0.1° × 0.1° resolution, daytime), events where dust particles (including dust and polluted dust) and clouds are simultaneously observed within the altitude range corresponding to temperatures from −10° to −40°C at each grid point are defined as potential aerosol-cloud interaction events [as illustrated in (B)]. The proportion of these potential aerosol-cloud interaction events in all dust events over the four spring seasons spanning 12 months are shown in (D) and (E). Solid black lines in (B) and (C) represent surface land. Dashed orange, sky-blue, and blue lines represent the isotherm lines at 0°, −20°, and −40°C, respectively. Note that the locations marked in (A) and (D), namely LHG (Laohugou), TS (Tianshan), DKMD (Dongkemadi), AL (Anglong), and ZF (Zhufeng, also known as Mt. Everest), correspond to the geographic positions of the snowpack samples analyzed in the subsequent experimental section.

The TP exhibited a substantial concentration and frequency of cloud ice in spring, as evidenced by CloudSat data (Fig. 2A), peaking at 26.3 liter−1 in May (Fig. 2B). The distribution of cloud ice was predominantly observed at altitudes ranging from 7 to 9 km AMSL, corresponding to environmental temperatures of −20° to −40°C. These conditions are typical for the formation of mixed-phase clouds, where primary ice production, driven by heterogeneous ice nucleation, predominates (1, 44, 45). In spring, the increasing atmospheric water vapor provided increasingly favorable conditions for cloud ice formation (Fig. 2C). In addition, aerosol loading reached its seasonal peak during this period (Fig. 2D). Three aerosol products were analyzed: the aerosol optical depth (AOD, a proxy for aerosol loading, obtained from MODIS), the dust optical depth (DOD, a proxy for dust loading, obtained from MODIS), and the nonspherical AOD [(nsAOD, representing the abundance of nonspherical particles such as dust, obtained from the Multi-angle Imaging SpectroRadiometer (MISR)]. All these aerosol products displayed monthly variations similar to the cloud ice number concentration, indicating the dominance of dust particles over the TP and their link to dust INPs and cloud ice. The peaks in nsAOD and cloud ice number concentration were observed in May (Fig. 2, B and D). Moreover, the monthly averaged AOD, DOD, and nsAOD were significantly and positively correlated with cloud ice number concentration, showing statistical significance (AOD, R = 0.85, P < 0.01; DOD, R = 0.54, P = 0.07; nsAOD, R = 0.96, P < 0.01; n = 12; Fig. 2E). All these findings substantiate that dust particles can act as INPs and regulate ice formation in mixed-phase clouds over the TP region during spring.

Fig. 2. Cloud ice number concentration and AOD over the TP.

(A) Variation of the daily averaged vertical distribution of cloud ice number concentration over the TP from June 2006 to July 2019. The three dashed gray lines from the bottom up in the figure represent the altitudes corresponding to 0°, −20°, and −40°C, respectively. The multiyear monthly variations of cloud ice number concentration (B), atmospheric water vapor (C), AOD [(D), red dot-dashed lines], DOD [(D), orange dot-dashed lines), and nsAOD, [(D), blue dot-dashed lines] over the TP. Dot-dashed lines in (B), (C), and (D) indicate the monthly means from June 2006 to July 2019. (E) Relationship of monthly averaged AOD (solid red circles), DOD (solid orange circles), and nsAOD (solid blue circles) with cloud ice number concentration from June 2006 to July 2019. Note that the monthly averaged cloud ice number concentration shown in (B) and (E) is derived from the median daily values within the altitude range corresponding to temperatures from −10° to −40°C, as depicted in (A). Dashed lines are plotted through a linear regression model. Error bars are the SDs.

The high-concentration cloud ice (>10 liter−1) appearing at altitudes up to 15 km AMSL during the summer months of July and August can be primarily attributed to the homogeneous nucleation mechanism. In the TP region, atmospheric water vapor reached its peak in August, measuring 0.94 cm (Fig. 2C and table S3). Concurrently, there was a gradual monthly increase in surface temperature, contributing to the development of an increasingly unstable atmosphere. Such atmospheric conditions were favorable for atmospheric convection, providing the necessary environment for homogeneous nucleation. In addition, the convective processes also enhanced the vertical transport of aerosols, leading to a competition between heterogeneous and homogeneous nucleation. Consequently, over the TP during summer, primary ice production, including both heterogeneous and homogeneous nucleation, predominated in cloud ice formation at temperatures below −20°C.

Summarizing findings from multisource satellite-based observations, we examine the role of dust particles in the TP as INPs regulating cloud ice formation during spring. In the TP region, atmospheric water vapor gradually increases after March, concurrent with a continuous rise in aerosols, predominantly composed of dust particles. These aerosols are uplifted by ascending air currents to cloud height, where they act as INPs, initiating large-scale ice crystal generation. Such activities peak in May. Subsequently, despite the increasing abundance of water vapor, the atmospheric loading of dust particles gradually decreases, resulting in less abundant dust INPs and less cloud ice formation.

INP concentrations and sources over the TP

During the summer of 2020, snowpack samples were collected from five glaciers located in the TP region, namely LHG (Laohugou), TS (Tianshan), DKMD (Dongkemadi), AL (Anglong), and ZF (Zhufeng, also known as Mt. Everest), as detailed in figs. S1 and S4 and table S4. These samples were analyzed to determine the concentrations and sources of atmospheric INPs over the TP. We measured the INP concentrations per volume of sample-melt water (NINP_water) directly and estimated the INP concentrations per volume of air (NINP_air) by assuming a cloud water content (CWC) of 0.15 g m−3 (46), following the procedure outlined by Petters and Wright (47) (Materials and Methods). Note that the reported NINP_water and NINP_air in this study should be viewed as upper limits, considering the impact of wet and dry deposition, as well as other inevitable uncertainties. However, despite these limitations, the conclusions of this study remain valid and robust (as discussed in the Materials and Methods).

The NINP_air were 1.26 ± 1.43, 0.65 ± 0.61, 0.26 ± 0.22, 0.10 ± 0.10, and 0.02 ± 0.01 liter−1 air (means ± SD) at −20°C for LHG, TS, DKMD, AL, and ZF, respectively, indicating a spatial distribution of INP concentrations, with higher concentrations in the north and lower in the south. Such distribution aligns with the spatial characteristics of DOD. In other words, the observed NINP_air distribution, implying INPs with significant variations in INA from different sources, correlates with the characteristic spatial patterns of deserts and dust loading within the same geographic region [Fig. 3, A and B, and figs. S5 to S7; P < 0.01, based on a one-way analysis of variance (ANOVA)]. The dust sources affecting the TP include local emissions and semiarid and arid areas in East Asia as near sources, with those in the Central Asia, Middle East, and North Africa serving as remote sources (48, 49). In particular, the Taklamakan Desert and the Qaidam Basin Desert to the north, as well as the Thar Desert to the southwest of the Plateau, are crucial contributors to the region’s dust (50). Although samples collected from the same site presented relatively similar freezing properties under conditions relevant for mixed-phase clouds, considerable differences in INP concentrations (NINP_water and NINP_air) were observed among samples collected from different locations, ranging up to three orders of magnitude. Nonetheless, despite these variations, all 47 samples were within the ice nucleation envelope curves derived from worldwide precipitation samples (47). The NINP_air at −20°C in the TP region had a median of 0.20 liter−1 air and a mean of 0.51 liter−1 air. These values are comparable to those measured in the Arctic (51–68), European regions (69), and North America (47) but substantially higher than those measured over the Southern Ocean (Fig. 3C) (34). The exceptions are the samples collected from ZF, which as a group exhibited the lowest INP concentrations with a median of 0.009 liter−1 air. Such low INP concentrations are comparable to the low INP concentration values measured by ice core (51, 52), ground-based air samples (57, 60, 62), and ship-borne oceanic air observations (65) in the Arctic region.

Fig. 3. INP concentrations of five glaciers over the TP.

(A) INP concentrations per volume of sample-melt water (NINP_water) and per volume of air (NINP_air) as a function of temperature for the five glaciers—LHG, TS, DKMD, AL, and ZF—represented by solid colored circles. INP envelope curves of global precipitation provided by Petters and Wright (47) (dashed black lines) and background INP spectrum (distilled water, solid sky-blue circles) are shown for comparison. (B) Spatial distribution of NINP_air at −20°C indicated by the size of circles, with map color representing the DOD at 550 nm. (C) Comparison of INP concentrations at −20°C between the TP and other parts of the world. The measurement locations and sample types are indicated in text. The boxes represent the interquartile range (IQR, IQR = Q3 − Q1). The whiskers represent the range within 1.5 * IQR. The solid lines, solid circles, and solid diamonds represent the median, mean, and outlier, respectively. Values greater than Q3 + (1.5 * IQR) or less than Q1 − (1.5 * IQR) are defined as outliers.

Hydrogen and oxygen isotope analyses showed that all snowpack samples fell on the global meteoric water line (GMWL) (70), indicating that they originated from atmospheric precipitation without substantial evaporation. In other words, the snowpack samples were not notably concentrated or enriched. There was no correlation between INP concentration and hydrogen and oxygen isotopes, suggesting that INPs were caused by aerosols and were not directly related to the moisture source (fig. S8). The NINP_air at −20°C was strongly correlated with the concentrations of representative chemical elements (Ca, Mg, Al, K, Mn, Fe, and Sr) in crustal minerals with statistical significance (R ≥ 0.65, P < 0.01, n = 20) (Fig. 4A and table S5). Taking calcium (Ca) as an example, high Ca concentrations in the snowpack samples from LHG and AL indicated the presence of large amounts of dust particles at both locations. The INP concentration was positively and significantly correlated with the Ca content (R = 0.83, P < 0.01) (Fig. 4B). On the other hand, the ratio of water-soluble calcium ion to elemental calcium (Ca2+/Ca) was significantly negatively correlated with the INP concentration (R = −0.84, P < 0.01) (Fig. 4C). This implies that particulate calcium, indicative of dust particles, is a more decisive factor in determining INP concentration than water-soluble calcium. The presence of biological or organic components attached to mineral dust particles, potentially indicative of a source from fertile regions, may enhance their INA [e.g., (61, 71)]. Wet heat treatment at 95°C for 20 min was found to reduce the concentrations of highly active INPs at temperatures higher than or equal to −15°C, where heat-labile INPs prevailed, constituting 58.1 to 100% of the total INPs (figs. S7B and S9). At −20°C, heat-resistant INPs contributed 38.5 to 58.7%. Notably, mineral components such as quartz, plagioclase feldspars, and clay-based minerals lose their INA when subjected to wet heat, potentially leading to misidentification as biogenic INPs (72). Hence, while this result may indicate the role of fertile region sources, caution should be exercised when interpreting heat-labile INPs in snowmelt samples containing abundant mineral dust, as they may not exclusively represent proteinaceous biological INPs. In this study, the term “dust particles” encompasses the potentially attached biological or organic components along with mineral dust, providing a comprehensive characterization of the observed phenomena. Our findings indicate that dust particles were the main source of atmospheric INPs over the TP.

Fig. 4. Source analysis of the measured INPs over the TP.

(A) Correlation analysis between INP concentrations per volume of air (NINP_air) at −20°C and representative crustal elements [calcium (Ca), magnesium (Mg), aluminum (Al), potassium (K), manganese (Mn), iron (Fe), and strontium (Sr)]. The correlation coefficient (R) is represented numerically and in color, with three asterisks above the value indicating statistically significant correlations (P < 0.01, two-tailed Pearson correlation test, n = 20). (B) Relationship of NINP_air at −20°C with elemental Ca for snowpack samples from LHG (solid orange circles) and AL (solid blue circles). Dashed black lines are plotted through a linear regression model. (C) The same as (B) but applied to the relationship of NINP_air at −20°C with the ratio of water-soluble calcium ion to elemental calcium (Ca2+/Ca). (D) 72-hour backward trajectory analysis of precipitation air mass (solid colored lines) combined with land cover types (colored map). The precipitation trajectories are clustered, and each cluster is colored according to the proportion of trajectories contained within it. Major deserts surrounding the sampling sites are labeled on the map.

The importance of dust particles for INPs was also supported by integrating air backward trajectory analysis with land cover types (Materials and Methods). In LHG, where the INP concentration was the highest among the five glaciers on the TP, 77.2% of the 72-hour backward trajectories of precipitating air mass passed through barren land surfaces, while in ZF with the lowest INP concentration, this proportion was only 10.4%. These precipitating air masses were primarily uplifted from near the surface to the initial trajectory point at an altitude of 1750 m above ground level (AGL), with median altitudes ranging from 800- to 1650-m AGL, suggesting their occurrence mainly within the atmospheric boundary layer (Fig. 4D, figs. S10 to S13, and tables S6 to S8). Consistent results were obtained from the analyses of 120-hour and 168-hour backward trajectories, further confirming the strong association between INP concentrations and dust sources (figs. S14 and S15 and tables S9 and S10). Considering that barren and grassland (shrubland) surfaces constitute the primary land cover types in the TP region (36.8 and 56.5%, respectively), we conclude that higher proportions of trajectories over these surfaces correspond to higher INP concentrations (fig. S16 and tables S11 and S12). However, we acknowledge that trajectory analysis should be interpreted cautiously, providing semiquantitative insights into INP sources due to real-world complexities and potential deviations in simulation calculations.

Long-term trends of CRE over the TP

From 2007 to 2019, the spring DOD in the TP region exhibited a statistically significant decreasing trend of −0.0021 ± 0.0007/year (Fig. 5A, P = 0.015), similarly to AOD and nsAOD, both of which also showed significant declines (all P ≤ 0.031; fig. S17, A and B). Springtime dust activity in the TP has demonstrated a continuous decline over the past half-century, a trend consistent not only in the TP but also across the expansive regions of East Asia, West Asia, and South Asia throughout the 21st century (73). Decreased wind speeds, increased vegetation cover, and increased soil moisture, as well as anomalous atmospheric circulation, are identified as key factors contributing to this reduction (74, 75). Concurrently, the net total flux of CREs (SFC CRE NET) and the net total flux of top of the atmosphere CREs (TOA CRE NET), both derived from the Clouds and the Earth’s Radiant Energy System (CERES), also showed a similar decreasing trend over the same period (all P < 0.1, based on the Mann-Kendall monotonic trend test), with a negative trend of −0.60 ± 0.14 and −0.43 ± 0.16 W m−2 year−1, respectively (Fig. 5B, fig. S17C, and tables S13 and S14).

Fig. 5. CREs and identification of their key influencing factors over the TP.

(A) Interannual variation of DOD over the TP from March to May in spring, 2007 to 2019. The solid black circles represent the spring means, and the error bars indicate the range of extremes. The solid red line represents the interannual variation trend obtained from the linear regression by the least squares method, and the light red area represents the 95% confidence band of the linear regression. (B) The same as (A) but applied to the SFC CRE NET. (C) The impact of atmospheric features on the predicted SFC CRE NET using SHAP values derived from an interpretable machine learning model (XGBoost integrated with 10-fold cross-validation and SHAP). The model performance is assessed by mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). (D) The same as (C) but analyzed by partial least squares regression model. (E) Pearson correlation analysis of SFC CRE NET and the influencing atmospheric features. (F) The same as (E) but analyzed by partial correlation method. The Pearson and partial correlation coefficients (R) are represented numerically and in color, with one, two, and three asterisks above the values indicating statistical significance at the 90, 95, and 99% confidence levels, respectively (n = 13). The meanings of the analytical method and feature parameters are: SHAP (SHapley Additive exPlanations), SFC (SFC CRE NET), CF (cloud fraction), CBH (cloud base height), CTH (cloud top height), CTT (cloud top temperature), CIWP (cloud ice water path), and AWV (atmospheric water vapor).

Comprehensive analyses involving aerosol properties (DOD, AOD, and nsAOD), meteorological conditions (atmospheric water vapor), and cloud characteristics (cloud fraction, cloud base and top heights, cloud top temperature, and cloud ice water path) identified aerosols as the primary drivers of variations in both SFC and TOA net CRE. This conclusion was consistently supported by four distinct analytical methods: an interpretable machine learning model (XGBoost with 10-fold cross-validation and Shapley additive explanations (SHAP), Fig. 5C and fig. S18), partial least squares regression (Fig. 5D and figs. S19 and S20), Pearson correlation (Fig. 5E and fig. S21), and partial correlation (Fig. 5F and fig. S22). Among these, DOD was determined to be the optimal parameter representing dust particles, showing the highest statistical significance and precision in its influence on CREs.

By integrating field observations of INP concentrations with an attribution analysis of dust sources, our findings confirm the substantial influence of dust particles on INP levels over the TP. Furthermore, we provide compelling observational evidence that dust INPs in the Third Pole region have notable potential to alter cloud microphysical processes and influence radiative effects. The observed decrease in aerosol loading, specifically dust particles, since 2007 potentially resulted in a reduction in springtime INPs. Coupled with the identification of dust particles as the dominant factor, we hypothesize that this reduction led to a simultaneous decrease in net CRE at SFC and TOA, producing a cooling effect (Fig. 6, A to D; n = 13). In other words, the decrease in dust INPs resulted in an enhanced cloud cooling effect, with a decrease of 0.01 in DOD leading to a reduction of 1.98 ± 0.39 W m−2 in surface net CRE over the TP region. Considering the widespread use and accessibility of AOD, the relationship between AOD and CRE is also presented. The deviation between the surface net CRE obtained from satellite-based measurements and those estimated using DOD, AOD, and nsAOD was within a factor of 1 (table S15 and fig. S23). This further confirmed the reliability of our conclusion. Our hypothesis is consistent with previous studies suggesting that the increase in INPs can cause a warming effect by affecting the microphysical processes of mixed-phase clouds (7, 76).

Fig. 6. Relationship between CREs and AOD over the TP.

(A) Relationship between the SFC CRE NET and DOD in spring 2007 to 2019. (B) Relationship between the mean TOA CRE NET and DOD in spring 2007 to 2019. (C) The same as (A) but applied to AOD. (D) The same as (B) but applied to AOD. Solid squares are marked with years, and solid black lines are plotted through a linear regression model. Correlation coefficient (R) is calculated by two-tailed Pearson correlation test (n = 13).

DISCUSSION

The principal contribution of our study, as illustrated in Fig. 7, is the demonstration that springtime dust particles over the TP act as INPs, resulting in the occurrence of seasonally elevated cloud ice concentrations and consequentially notably modifying the CRE in this region. We provide the first spatial distribution characteristics of INP concentrations over the TP and attribute the observed spatial heterogeneity primarily to dust particles as the leading driving factor. The notable decrease in springtime dust particles in the TP region from 2007 to 2019, along with the expected resulting reduction in dust INPs, may provide a convincing explanation for the enhancement of cloud cooling effect.

Fig. 7. Schematic of dust particles acting as INPs to alter the CREs over the TP.

Our findings bear important implications for ongoing research in the atmosphere, cryosphere, ecology, and climate changes, particularly in the TP and surrounding areas. The mechanism validated over the TP, where dust particles act as INPs to alter cloud microphysical processes and influence CRE, holds relevance for various global regions. Accurately predicting INPs and their impact on clouds in models remains a formidable challenge, especially in geographically and climatically unique regions such as the TP. Therefore, vertical and horizontal measurements of INPs, as well as long-term observations, are crucial for identifying their concentrations, sources, and direct interactions with clouds, thereby enhancing the scientific understanding of aerosols’ direct and indirect radiative effects. Moreover, Sarangi (77) proposed a positive feedback mechanism involving dust and the melting of high-altitude snow. When there is a decrease in atmospheric dust loading, both the dust-induced snow albedo effects and the indirect radiative effects of dust as INPs contribute to a cooling effect, mitigating the warming. However, in a warmer climate, expanded bare soil with deglaciation and elevated biological activity may promote the emission of dust and biological aerosols, raising INPs supplies, affecting cloud formation and radiation characteristics, and accelerating the warming process.

MATERIALS AND METHODS

Sample collection

A total of 47 samples were obtained from the permanent snowpack on five glaciers distributed throughout the whole TP region (fig. S1), including: LHG (39.4°N, 96.6°E, 4990 m AMSL, 10 samples), TS (43.1°N, 86.8°E, 4070 m AMSL, 12 samples), DKMD (33.1°N, 90.1°E, 5708 m AMSL, 10 samples), AL (32.8°N, 80.9°E, 5967 m AMSL, 10 samples), and Mt. Everest, ZF (35.7°N, 94.2°E, 6525 m AMSL, 5 samples). Sampling was carried out during the summer of 2020, which is close to the peak snowpack period, with snow accumulating primarily in the spring and the previous winter. We selected the sampling sites based on the long-term field glacier observation stations established by the Chinese Academy of Sciences [e.g., (78, 79)], which are located far from any potential sources of human contamination. Professional researchers sampled the snowpack at each site down to the underlying hard ice surface, using disposable laboratory supplies, precleaned equipment, and protective clothing (fig. S4). After recording the snowpack stratigraphy, snowpack samples were separated and collected at vertical intervals of ~10 cm, as listed in table S4, and packed into disposable Whirl-Pak bags. All samples were kept frozen at −20°C until analysis.

INP measurements and calculations

Snow serves as an effective scavenger of atmospheric INPs since it is formed within clouds, falls through the atmosphere, deposits on the ground, and subsequently accumulates (80). Therefore, we adopt a method of freezing measurement of snowpack samples as an effective means to characterize atmospheric INPs in clouds (81, 82) over the TP.

The snowmelt water samples, i.e., the suspension containing aerosol particles, were used for subsequent INP measurements and chemical analyses. Following the methodology detailed in Chen et al. (83), the Peking University Ice Nucleation Array, a cold-stage–based instrument, was used for immersion freezing measurements. In brief, 90 droplets (1 μl) of suspensions were pipetted onto a hydrophobic glass slide positioned on the cold stage for each experiment. To prevent the Wegener-Bergeron-Findeisen process, the droplets were separated by a spacer block and upper and lower glass slides. The cooling process of the cold stage (cooling rate of 1°C min−1) was monitored using a charge-coupled device (one frame every 6 s) until all 90 droplets were frozen (84). The recorded images were analyzed by a machine learning–based Python program to identify and count the frozen droplets at each cooling temperature.

The cumulative number concentration of INPs per unit volume of sampled snowmelt water (NINP_water) (85) is calculated asNINP_water T=−ln1−ficeTVdroplet liter−1 water(1)

where fice(T) is the fraction of frozen droplets in the total 90 droplets above temperature T, and Vdroplet is the volume of one experimental droplet (1 μl in this study).

To facilitate comparisons with previous studies, according to Petters and Wright (47), we estimated the cumulative number concentration of INP per unit volume of atmospheric air (NINP_air) from NINP_water through the CWC, i.e., the quantity of precipitable water per unit volume of air.NINP_air T=NINP_water T×CWC liter−1 air(2)

The NINP_air was determined using a CWC value of 0.15 g m−3, obtained from the average value of an aircraft measurement of CWC over the TP, with values ranging from 0.03 to 0.25 g m−3 (46). This value was corroborated by research that estimated the CWC in precipitating convective clouds through multiyear satellite observations, revealing that the CWC was less than 0.2 g m−3 (86). While Petters and Wright (47) used a CWC of 0.4 g m−3 to determine atmospheric INP concentrations in their study, the most appropriate value is one that is representative for the cloud scenarios leading to snowfall deposition, and the estimated uncertainties in the comparison in Fig. 3 (A to C) may be within a factor of 3.

The snowmelt samples were remeasured after wet heat treatment (95°C for 20 min). By subtracting the concentration of heat-resistant INPs, which represent components that retain freezing activity under wet heat, we evaluated the contribution of heat-labile INPs. These heat-labile INPs are primarily composed of proteinaceous biological materials, potentially along with certain mineral components that lose their INA under wet heat conditions (8, 72).

Chemical analysis

Hydrogen and oxygen isotopes and water-soluble ions were analyzed for all 47 snowmelt water samples (suspensions). Measurement of oxygen-18 (δ18O) and deuterium (δD) isotopes in precipitation is useful for tracing climate and hydrological processes, helping to understand the sources of precipitation, evaporation history, and hydrological cycling in the region (87). These two isotopes in liquid samples were measured using the L2130-i Isotope and Gas Concentration Analyzer (Picarro Inc.). To analyze water-soluble calcium ion (Ca2+), the suspension was first filtered through a polyethersulfone membrane filters with a pore size of 0.45 μm. The filtered solution was then detected by ion chromatography with DIONEX ICS-2000/Integrion instruments (Thermo Fisher Scientific Inc.).

Because of the limited sampling volume, a total of 20 samples from two representative sites, LHG and AL, were selected to analyze the elemental compositions (Ca, Mg, Al, K, Mn, Fe, and Sr). The suspensions containing particles were digested using a microwave acid digestion system (MARS, CEM Inc., USA) with nitric acid (65%, analytical reagent, Merck Inc., Germany) until complete digestion of all particles. The samples were then determined by the inductively coupled plasma mass spectrometry (XSeries 2, Thermo Fisher Scientific Inc.).

Backward trajectory and land cover analysis

Backward trajectories of air masses reaching the five sampling sites were separately calculated using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory, Version 5.1.0) model (88), facilitated by the PySPLIT Python package (89). The archive trajectories were computed with meteorological input data from the Global Data Assimilation System (1°, global). The collected snowpack samples were primarily accumulated during the spring of 2020 and the preceding winter. To analyze the primary sources and transportation pathways of precipitating air masses, 72-, 120-, and 168-hour backward trajectories were generated for each of the five sampling sites from June 2019 to May 2020, with a time resolution of 3 hours and a starting altitude of 1750-m AGL. Subsequently, these trajectories were filtered to retain only those with precipitation detected at the initial point, accounting for 11.6 to 30.8% of the total trajectories (2928 trajectories, 8 trajectories per day × 366 days) (table S6). Surface land is the only source of dust particles. On the basis of the aforementioned 72-hour backward trajectories, an additional screening criterion of within 1-km AGL was added to consider the uplift of surface aerosols. In determining the initial altitude of trajectories, we calculated and compared the temperatures at altitudes of 500-, 1000-, 1500-, and 1750-m AGL at five sampling sites (fig. S10). The median temperature range at the altitude of 1750-m AGL varied from −10° to −20°C, and their interquartile ranges aligned well with the freezing temperature observed in the measured snowpack samples. Therefore, for subsequent analysis in this study, we selected backward trajectories with an initial altitude of 1750-m AGL.

In addition, to evaluate the potential influence of surface aerosol emissions on the air masses, we integrated the backward trajectories with land cover types. The land cover types were classified into 17 categories defined by the International Geosphere Biosphere Programme using data from the Land Cover Type Yearly Climate Modeling Grid product for the year 2019, with a spatial resolution of 0.05° (MCD12C1 v006) (90). Each trajectory was matched with its corresponding latitude and longitude to determine the land cover type, and the proportion of seven land cover types (barren, grassland and shrubland, forest, cropland, water and wetland, snow and ice, and urban) was calculated for each trajectory (tables S8 to S10). Note that because of unavoidable uncertainties, the backward trajectory analysis combined with land cover types should be considered as a semiquantitative approach. However, this analysis enabled the identification of potential aerosol sources in precipitating air masses.

TP boundary

In this study, we defined the boundary of the TP region using an altitude threshold of 2500-m AMSL (fig. S1), encompassing all five sampling sites (LHG, TS, DKMD, AL, and ZF). The boundary data were obtained from the National Tibetan Plateau Data Center, which extracted elevation information at 2500-m AMSL for the TP from the Earth Topography Five-Minute Grid Global Surface Relief data within the longitudinal range of 65° to 105°E and latitudinal range of 20° to 45°N (91, 92). All satellite-based and reanalysis data used in this study were processed and analyzed within the defined boundary range of the TP region.

Satellite-based data sources and processing

This study used satellite-based data derived from several sources including CloudSat, the MODIS, the MISR, and the CERES. Because of the availability of valid data from CloudSat, unless otherwise specified, the temporal coverage of all analyzed satellite data in this study was limited to the period from June 2006 to July 2019, encompassing a total of 158 months. The data illustrated in Fig. 1 were obtained from the CALIPSO. The datasets used in this study are given in table S16.

The retrieved estimates of cloud ice number concentration for each radar profile measured by the Cloud Profiling Radar on CloudSat were obtained from the CloudSat Level 2B Radar-Only Cloud Water Content product (2B-CWC-RO, Version P1_R05). The particle size of detected ice crystals from CloudSat predominantly ranges from 25 to 2000 μm (93). The primary remote-sensing input for this radar-only product is the measured radar reflectivity factor (also referred to as “reflectivity”). The distinction between liquid and ice phases was made using temperature profiles from reanalysis products that were collocated with each radar profile. These temperature profiles were contained in the CloudSat European Centre for Medium-range Weather Forecast Auxiliary product (ECMWF-AUX, Version P1_R05) (40).

The level-3 Atmosphere Monthly Global Joint product MYD08_M3 of MODIS Collection 6.1 contained hundreds of 1° × 1° global gridded scientific datasets derived from the level-2 products of aerosol (04_L2), water vapor (05_L2), cloud (06_L2), and atmosphere profile (07_L2) (94), providing detailed atmospheric information over the TP. The data in MYD08 were acquired from the Aqua platform, with overpass times around 13:30 local solar time in ascending (daytime) mode and 01:30 local solar time in descending (nighttime) mode. This monthly level-3 (M3) product was generated from the entire collection of daily files corresponding to a particular month without any overlap.

AOD is a column integration of extinction coefficient by atmospheric particles. As one of the major improvements for the new version, a “Combined” Deep Blue + Dark Target AOD at 550 nm was computed by combining these two different retrieval algorithms to obtain improved estimates of AOD (95). This combined dataset results in more accurate and reliable estimates of AOD compared to using either algorithm alone. It also provides better spatial and temporal coverages of AOD. Therefore, we chose the Combined Dark Target + Deep Blue AOD product for analysis. The TP has a vast area and sparse population, where dust particles are the dominant type of aerosols. The AOD parameter is crucial in characterizing the distribution and atmospheric transport of dust particles over the TP. Moreover, to enhance the credibility, we analyzed the monthly DOD from MODIS onboard both the Terra and Aqua satellites, as well as the monthly nsAOD from the MISR instrument on Terra. Unlike traditional AOD measurements assuming spherical particles, nsAOD takes into account the complex shapes of real-world aerosols, such as dust (96). In the current study, the monthly nsAOD data were derived from the version 23, level 2, daily MISR 550-nm nsAOD at 4.4-km resolution (97). Since dust is the primary nonspherical aerosol particle in the atmosphere, especially over sparsely vegetated regions like the TP, the nsAOD represents the AOD due to dust. In addition, the MODIS DOD characterizes the optical depth of absorbing coarse-mode aerosols, which are typically dust particles found over bare ground or sparsely vegetated regions (98). Daily DOD is calculated from Collection 6.1 Level 2 MODIS Deep Blue spectral AOD mapped on a geographic 0.1° × 0.1° grid and subsequently transformed into a monthly product (99, 100).

Cloud product containing cloud-top properties (temperature and height) and cloud optical and microphysical properties (cloud fraction and ice water path) and water vapor product (precipitable water vapor) from MODIS were also analyzed in the present study. The algorithm of cloud top properties relies on CO2-slicing channels and two infrared (IR) window channels and has heritage with the high-resolution IR radiation sounder (101, 102). The cloud optical and microphysical product makes primary use of six visible, near-IR (NIR), shortwave-IR, and midwave-IR MODIS channels, as well as several thermal channels (103, 104). As a characterization of column water-vapor amounts, the water vapor product combines results from both NIR and IR algorithms (105, 106).

TOA and surface monthly means data collected using the CERES Scanner instruments on both the Terra and Aqua platforms (107, 108) were also examined. We focused on the long-term trend of CREs over the TP derived from the CERES Energy Balanced and Filled (EBAF) product (edition 4.1) (109). CREs are computed as all-sky flux minus clear-sky flux. The data have a spatial resolution of 1° × 1°, and the TOA data were directly observed, while the surface all-sky and clear-sky fluxes were calculated.

The CALIPSO satellite, known for its global observational capabilities, provides valuable data on aerosol and cloud profiles, enabling studies on the impact of clouds and aerosols on Earth’s climate (110). In this study, we used the CALIPSO level 2 Lidar Vertical Feature Mask (111) and 5-km Aerosol Profile products (Version 4.20) (112). These products include feature classification for cloud and tropospheric aerosols, tropospheric AOD measurements at 532 nm, and aerosol total backscatter coefficient at 532 nm. The AOD values are derived using a scene classification algorithm combined with the integrated aerosol extinction vertical profile at 532 nm (113). The aerosol total backscatter coefficients at 532 nm are obtained by summing the parallel and perpendicular backscatter measurements recorded by the CALIPSO satellite.

Four methods were used to identify and quantify the key influencing factors of surface and TOA net CRE: an interpretable machine learning model (XGBoost with 10-fold cross-validation and SHAP), partial least squares regression, Pearson correlation, and partial correlation. XGBoost is an efficient machine learning approach, providing a detailed decomposition of predictive factors for CRE (114, 115). Partial least squares regression simplifies the model by reducing predictors to a smaller set of uncorrelated components, here using two components, and highlights the most important variables (116). Pearson correlation assesses linear relationships between variables, while partial correlation measures the relationship between two variables, controlling for the effects of others, thus enhancing the specificity of the findings (117). These diverse analytical techniques collectively reinforce the conclusion that aerosols, specifically dust particles, predominantly drive the variations in both surface and TOA net CRE.

Reanalysis data sources and processing

In this study, reanalysis data from two sources were used: the fifth generation of the ECMWF atmospheric reanalysis (ERA5) and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2, Version 5.12.4). The data cover a time period of 158 months, spanning from June 2006 to July 2019.

ERA5 offers high spatiotemporal resolution data for global weather and climate variations. Here, we used the monthly average of the cloud base height parameter from ERA5 (118) to evaluate the cloud condition over the TP. This parameter is defined as the height of the lowest cloud base above the Earth’s surface at a specific time, with a horizontal resolution of 0.25° × 0.25°.

MERRA-2, produced by the NASA Global Modeling and Assimilation Office using the Goddard Earth Observing System model version 5.12.4, is the latest version of global atmospheric reanalysis for the satellite era (119). To assess the potential contribution of wet deposition over the TP, we used the monthly dry and wet deposition of dust particles for five size bins corresponding to specific dry size ranges (0.1 to 1.0, 1.0 to 1.8, 1.8 to 3.0, 3.0 to 6.0, and 6.0 to 10.0 μm). The horizontal resolution of MERRA-2 data used in this study is 0.5° × 0.625°.

Uncertainty in INP measurements and satellite-based results

The uncertainties in this study may arise from sample collection, experimental analysis, estimation of atmospheric INP concentration, and satellite observation and reanalysis data. These uncertainties are inherent, and, therefore, the present section discusses potential sources of uncertainties and efforts taken to minimize them during experimentation and data analysis.

As previously mentioned, each snowpack sampling point was located in a long-term glacier expedition area, taking into account research objectives, natural ecological environment, and human activities. Skilled researchers overcame numerous difficulties and used standard sampling procedures to collect vertically layered snowpack samples. All samples were stored in sterile disposable containers and frozen until subsequent analysis. Furthermore, the hydrogen and oxygen isotope analysis showed that all of the snowpack samples were on the GMWL, suggesting that they came from atmospheric precipitation with little to no evaporation (fig. S8). In other words, the INPs and various chemical components present in the snowpack samples were scarcely concentrated or diluted. Therefore, we believe that the samples collected from five glaciers were representative, and any effects from the sampling and transportation process have been minimized.

After calibrating the PKU-INA instrument, the temperature uncertainties at different cooling rates were found to be less than ±0.4°C (120). However, the contribution of nucleation stochasticity is larger, making it the main source of uncertainty in INP measurements. To calculate the confidence intervals for the number of INPs per unit volume (per droplet with 1 μl) in droplet freezing experiments, Poisson statistics is a better choice (121, 122). We used the method proposed by Barker (123) to calculate the confidence intervalsμT+Zα/222n±Zα24μ+Zα22n0.5/4n0.5(3)

where μ(T) is the number of INPs per droplet, n is the droplet number (90 droplets in this case), Zα/2 is the standard score at a confidence level α/2, which is equal to 1.96 for a 95% confidence interval.

Measurement of INP concentration in snowpack samples provides a means to estimate atmospheric INP concentration. While theoretically, the particles in the collected samples serve as the seeds for precipitation formation, in practice, the contribution of dry deposition cannot be ruled out, although wet deposition [encompassing both rainout (in-cloud scavenging) and washout (below-cloud scavenging)] is the primary mechanism for INP capture. During the spring season (March to May) from 2007 to 2019, the proportion of wet deposition for dust particles in the size range of 0.1 to 10.0 μm over the TP region ranged from 72.7 to 80.3% of the total deposition (dry deposition + wet deposition) (table S17). The impact of washout on the INP concentrations in ground-collected samples is limited. Vali (124) found that the spectral shapes of precipitation samples collected almost simultaneously at cloud base and on the ground were similar, with slightly higher INP concentrations in the cloud base samples. Wright (125) estimated that washout contributed 1.2 to 14% of the INPs in precipitation samples collected at the surface. In addition, hydrogen and oxygen isotope analysis showed that pre-concentration of snowpack samples could be ignored in this study. Therefore, this study did not correct for the effects of dry deposition, washout, and pre-concentration, and the measured INP concentrations should be treated as upper limits.

The satellite observations and reanalysis data are official products provided by their respective organizations. However, it is necessary to acknowledge the limitations of these products used in this study. First, the spatial resolution of some products is relatively coarse, typically 1° × 1°. Nevertheless, our analysis covered a vast area of the TP region, spanning from 63° to 105°E in longitude and from 25° to 45°N in latitude, which provided sufficient data points to analyze the trends in various parameters. Second, satellites acquire data along fixed orbits, but data may not always be resolved due to instrument failure or meteorological conditions. The use of monthly mean statistics helps to effectively fill in orbital gaps, allowing for complete coverage of the TP and enabling long-term trend analysis.

Third, different algorithms provide multiple parameters with similar but different usage conditions, such as AOD. The Dark Target algorithm is optimized for retrieving AOD over land surfaces with relatively low surface reflectance, while the Deep Blue algorithm is optimized for retrieving AOD over bright surfaces, such as deserts. Factors that can affect AOD retrievals include cloud presence, aerosol types and compositions, surface properties (such as roughness, vegetation cover, and snow and ice coverage), and sun-satellite geometry. Barren, grassland and shrubland, and forest land cover types dominate the TP, representing 96.6% of its surface area. In contrast, snow and ice cover is limited to 1.72% (table S11). To obtain more accurate AOD estimates that account for the diverse surfaces and atmospheric conditions over the TP region, we used a combination of the Dark Target and Deep Blue algorithms. This approach enables us to consider the varying surface reflectance characteristics and enhance the accuracy of our AOD retrievals (95). In addition, DOD and nsAOD are capable of characterizing dust particles and provide a dependable foundation for evaluating dust abundance through AOD over the TP. Note that the wavelengths used for AOD inversion in MODIS and CALIPSO are different, with MODIS using 550 nm and CALIPSO using 532 nm. With algorithm upgrades, the mean AOD differences between CALIPSO and MODIS (ocean) have been reduced in Version 4 compared to Version 3. However, the CALIPSO AOD estimates still tend to be lower compared to MODIS (126). In our study, we did not directly compare the AOD results from MODIS and CALIPSO, and, therefore, the differences between them do not affect the research conclusions. Regarding the CRE data obtained from CERES, because of the incorporation of the radiative transfer model in the retrievals, both surface and TOA CRE exhibit larger uncertainties compared to the radiative fluxes measured directly at the TOA (127).

Fourth, as a crucial component of this study, the cloud ice number concentration retrieved from CloudSat provides direct observational evidence that is most plausibly explained by heterogenous ice nucleation triggered by dust INPs. Although the cloud ice number concentration depicted in Fig. 2, B and E is limited to the monthly averaged median daily values within the altitude range corresponding to temperatures from −10° to −40°C, the potential influence of competition between heterogenous and homogeneous nucleation in the upper layers, as well as the cloud condensation nuclei invigoration effect and secondary ice production in the lower layers, cannot be completely ruled out because of meteorological dynamics (117, 128, 129). However, for the long-term statistical data analyzed from 2006 to 2019, the impact of these factors is limited, and the conclusions of our study are robust. The quantitative comparison between experimentally measured INP concentrations and satellite-observed cloud ice number concentrations is limited by the inherent constraints of both observational techniques and satellite retrievals, as well as their distinct temporal and spatial coverages. This discrepancy is a long-standing unresolved issue in cloud physics, which is not the key focus of our study and does not affect our conclusions.

Last, it is crucial to consider various types of data pertaining to aerosols and clouds. The retrieval of MODIS AOD is based on cloud-free observations, excluding pixels with remarkable cloud cover. Cloud ice number concentration and other cloud parameters are obtained specifically within identified cloud regions. TOA and surface CRE in EBAF Edition 4.1 are determined using clear-sky fluxes determined for the total region rather than solely relying on the cloud-free portions of a region. The comparison and integration of data from cloud-free and cloudy conditions are justified as they provide a more comprehensive understanding of aerosol-cloud interactions and enable more accurate assessments of radiative effects.

In conclusion, while there may still be some uncertainties, the utilization of satellite-based and reanalysis data in our study effectively captures the spatiotemporal variations in aerosols and clouds. The statistical analysis performed with these datasets is reliable, and any uncertainties associated with the data do not compromise the main findings of our study.

Acknowledgments

We are grateful to B. J. Murray from the University of Leeds and Y. Chen from the University of Birmingham for participation in the discussion of our paper. We gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and M. S. C. Warner for the Python toolkit for NOAA ARL’s HYSPLIT model.

Funding: This work was funded by the National Natural Science Foundation of China Creative Research Group Fund (22221004), the joint project (NSFC-STINT) founded by the National Natural Science Foundation of China, and the Swedish Foundation for International Cooperation (42011530121).

Author contributions: J.C. and Z.W. conceived the study. J.C. conducted the experiments, analyzed the experimental and satellite-based data, visualized the results, and wrote the manuscript with contributions from Z.W., Y. Yu, P.J.D., Y. Yan, C.Z., S.-M.L., T.Z., and M.H. J.X. and L.Z. provided the snowpack samples. X.M. participated in the experiments and data analysis. P.G. and Y. Yu provided the DOD and nsAOD data. R.X. participated in CALIPSO data processing. All authors contributed to the writing of the manuscript.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The datasets analyzed and presented in this study are all publicly available, including the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) Version 6 data product (MCD12C1 v006) acquired from the Land Processes Distributed Active Archive Center (LP DAAC, https://lpdaac.usgs.gov/products/mcd12c1v006/); the Level-3 MODIS Atmosphere Monthly Global Joint product (MYD08_M3, Aqua platform, Collection 6.1) acquired from the Level-1 and Atmosphere Archive and Distribution System Distributed Active Archive Center (LAADS-DAAC, https://ladsweb.modaps.eosdis.nasa.gov/); the CloudSat Radar-Only Cloud Water Content product (2B-CWC-RO, Version P1_R05) and the CloudSat European Centre for Medium-range Weather Forecast Auxiliary dataset (ECMWF-AUX, Version P1_R05) acquired from the CloudSat Data Processing Center (CloudSat DPC, https://www.cloudsat.cira.colostate.edu/); the ERA5 monthly averaged reanalysis on single levels provided by ECMWF (https://cds.climate.copernicus.eu); the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) product (Edition 4.1) and the Multi-angle Imaging SpectroRadiometer (MISR) aerosol products acquired from the NASA Langley Research Center Atmospheric Science Data Center (ASDC, https://asdc.larc.nasa.gov/); the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 Lidar Vertical Feature Mask (CAL_LID_L2_VFM-Standard-V4-20) and 5 km Aerosol Profile products (CAL_LID_L2_05kmAPro-Standard-V4-20) obtained at https://www-calipso.larc.nasa.gov/products/; the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2, M2TMNXADG, Version 5.12.4) reanalysis data acquired from the Goddard Earth Sciences Data and Information Services Center (GES DISC, https://disc.gsfc.nasa.gov/); and the Tibet Plateau boundary dataset provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).

Supplementary Materials

This PDF file includes:

Figs. S1 to S23

Tables S1 to S17
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