Articles | Volume 16, issue 4
https://doi.org/10.5194/esd-16-1237-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-16-1237-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Diagnosing aerosol–meteorological interactions on snow within Earth system models: a proof-of-concept study over High Mountain Asia
Chayan Roychoudhury
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
Cenlin He
Research Applications Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
Rajesh Kumar
Research Applications Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
Avelino F. Arellano Jr.
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
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John Motley McKinnon, Chayan Roychoudhury, and Avelino Florentino Arellano Jr.
EGUsphere, https://doi.org/10.5194/egusphere-2024-3440, https://doi.org/10.5194/egusphere-2024-3440, 2025
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We explore the use of a statistical method called EOF analysis to analyze complex data, focusing on its strengths and limitations. While this method is widely used in climate research, its use in atmospheric chemistry is relatively new. We found that while EOF analysis can be powerful, it may not always be suitable for datasets that do not follow specific statistical assumptions. Our research provides recommendations to improve how we use this technique in analyzing atmospheric chemistry data.
Kavitha Mottungan, Chayan Roychoudhury, Vanessa Brocchi, Benjamin Gaubert, Wenfu Tang, Mohammad Amin Mirrezaei, John McKinnon, Yafang Guo, David W. T. Griffith, Dietrich G. Feist, Isamu Morino, Mahesh K. Sha, Manvendra K. Dubey, Martine De Mazière, Nicholas M. Deutscher, Paul O. Wennberg, Ralf Sussmann, Rigel Kivi, Tae-Young Goo, Voltaire A. Velazco, Wei Wang, and Avelino F. Arellano Jr.
Atmos. Meas. Tech., 17, 5861–5885, https://doi.org/10.5194/amt-17-5861-2024, https://doi.org/10.5194/amt-17-5861-2024, 2024
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A combination of data analysis techniques is introduced to separate local and regional influences on observed levels of carbon dioxide, carbon monoxide, and methane from an established ground-based remote sensing network. We take advantage of the covariations in these trace gases to identify the dominant type of sources driving these levels. Applying these methods in conjunction with existing approaches to other datasets can better address uncertainties in identifying sources and sinks.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
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Atmos. Chem. Phys., 25, 5591–5616, https://doi.org/10.5194/acp-25-5591-2025, https://doi.org/10.5194/acp-25-5591-2025, 2025
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We assess the contributions of fire and anthropogenic emissions to O3 levels in Phoenix, Arizona, during a period of intense heat and drought conditions. We find that fire exacerbates O3 pollution and that interactions between weather, climate, and air chemistry are important to consider. This has implications for activities related to formulating emission reduction strategies in areas that are currently understudied yet becoming relevant due to reports of increasing global aridity.
Genevieve Rose Lorenzo, Luke D. Ziemba, Avelino F. Arellano, Mary C. Barth, Ewan C. Crosbie, Joshua P. DiGangi, Glenn S. Diskin, Richard Ferrare, Miguel Ricardo A. Hilario, Michael A. Shook, Simone Tilmes, Jian Wang, Qian Xiao, Jun Zhang, and Armin Sorooshian
Atmos. Chem. Phys., 25, 5469–5495, https://doi.org/10.5194/acp-25-5469-2025, https://doi.org/10.5194/acp-25-5469-2025, 2025
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Novel aerosol hygroscopicity analyses of CAMP2Ex (Cloud, Aerosol, and Monsoon Processes Philippines Experiment) field campaign data show low aerosol hygroscopicity values in Southeast Asia. Organic carbon from smoke decreases hygroscopicity to levels more like those in continental than in polluted marine regions. Hygroscopicity changes at cloud level demonstrate how surface particles impact clouds in the region, affecting model representation of aerosol and cloud interactions in similar polluted marine regions with high organic carbon emissions.
Rajesh Kumar, Piyush Bhardwaj, Cenlin He, Jennifer Boehnert, Forrest Lacey, Stefano Alessandrini, Kevin Sampson, Matthew Casali, Scott Swerdlin, Olga Wilhelmi, Gabriele G. Pfister, Benjamin Gaubert, and Helen Worden
Earth Syst. Sci. Data, 17, 1807–1834, https://doi.org/10.5194/essd-17-1807-2025, https://doi.org/10.5194/essd-17-1807-2025, 2025
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We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS). The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county-level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
Parag Joshi, Tzu-Shun Lin, Cenlin He, and Katia Lamer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1751, https://doi.org/10.5194/egusphere-2025-1751, 2025
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Study revisits urban representation (using canopy models & bulk parameterization) in the Weather Research & Forecasting model. We propose methods to identify evaluable parameters via field measurements and found inconsistencies between UCM physics and code implementation. Simulations reveal small errors can significantly impact outputs, highlighting the need for precise physics implementation.
Mohamed Hamitouche, Giorgia Fosser, Alessandro Anav, Cenlin He, and Tzu-Shun Lin
Hydrol. Earth Syst. Sci., 29, 1221–1240, https://doi.org/10.5194/hess-29-1221-2025, https://doi.org/10.5194/hess-29-1221-2025, 2025
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This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
Cenlin He, Tzu-Shun Lin, David M. Mocko, Ronnie Abolafia-Rosenzweig, Jerry W. Wegiel, and Sujay V. Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-4176, https://doi.org/10.5194/egusphere-2024-4176, 2025
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This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Dylan Jones, Lucas Prates, Zhen Qu, William Cheng, Kazuyuki Miyazaki, Takashi Sekiya, Antje Inness, Rajesh Kumar, Xiao Tang, Helen Worden, Gerbrand Koren, and Vincent Huijen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3759, https://doi.org/10.5194/egusphere-2024-3759, 2025
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We evaluate five chemical reanalysis products to assess their potential to provide useful information on tropospheric ozone variability. We find that the reanalyses produce consistent information on ozone variations in the free troposphere, but have large discrepancies at the surface. The results suggests that improvements in the reanalyses are needed to better exploit the assimilated observations to enhance the utility of the reanalysis products at the surface.
John Motley McKinnon, Chayan Roychoudhury, and Avelino Florentino Arellano Jr.
EGUsphere, https://doi.org/10.5194/egusphere-2024-3440, https://doi.org/10.5194/egusphere-2024-3440, 2025
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We explore the use of a statistical method called EOF analysis to analyze complex data, focusing on its strengths and limitations. While this method is widely used in climate research, its use in atmospheric chemistry is relatively new. We found that while EOF analysis can be powerful, it may not always be suitable for datasets that do not follow specific statistical assumptions. Our research provides recommendations to improve how we use this technique in analyzing atmospheric chemistry data.
Kavitha Mottungan, Chayan Roychoudhury, Vanessa Brocchi, Benjamin Gaubert, Wenfu Tang, Mohammad Amin Mirrezaei, John McKinnon, Yafang Guo, David W. T. Griffith, Dietrich G. Feist, Isamu Morino, Mahesh K. Sha, Manvendra K. Dubey, Martine De Mazière, Nicholas M. Deutscher, Paul O. Wennberg, Ralf Sussmann, Rigel Kivi, Tae-Young Goo, Voltaire A. Velazco, Wei Wang, and Avelino F. Arellano Jr.
Atmos. Meas. Tech., 17, 5861–5885, https://doi.org/10.5194/amt-17-5861-2024, https://doi.org/10.5194/amt-17-5861-2024, 2024
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A combination of data analysis techniques is introduced to separate local and regional influences on observed levels of carbon dioxide, carbon monoxide, and methane from an established ground-based remote sensing network. We take advantage of the covariations in these trace gases to identify the dominant type of sources driving these levels. Applying these methods in conjunction with existing approaches to other datasets can better address uncertainties in identifying sources and sinks.
Connor J. Clayton, Daniel R. Marsh, Steven T. Turnock, Ailish M. Graham, Kirsty J. Pringle, Carly L. Reddington, Rajesh Kumar, and James B. McQuaid
Atmos. Chem. Phys., 24, 10717–10740, https://doi.org/10.5194/acp-24-10717-2024, https://doi.org/10.5194/acp-24-10717-2024, 2024
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We demonstrate that strong climate mitigation could improve air quality in Europe; however, less ambitious mitigation does not result in these co-benefits. We use a high-resolution atmospheric chemistry model. This allows us to demonstrate how this varies across European countries and analyse the underlying chemistry. This may help policy-facing researchers understand which sectors and regions need to be prioritised to achieve strong air quality co-benefits of climate mitigation.
Matthew S. Johnson, Sajeev Philip, Scott Meech, Rajesh Kumar, Meytar Sorek-Hamer, Yoichi P. Shiga, and Jia Jung
Atmos. Chem. Phys., 24, 10363–10384, https://doi.org/10.5194/acp-24-10363-2024, https://doi.org/10.5194/acp-24-10363-2024, 2024
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Satellites, like the Ozone Monitoring Instrument (OMI), retrieve proxy species of ozone (O3) formation (formaldehyde and nitrogen dioxide) and the ratios (FNRs) which can define O3 production sensitivity regimes. Here we investigate trends of OMI FNRs from 2005 to 2021, and they have increased in major cities, suggesting a transition from radical- to NOx-limited regimes. OMI also observed the impact of reduced emissions during the 2020 COVID-19 lockdown that resulted in increased FNRs.
Andrew O. Langford, Raul J. Alvarez II, Kenneth C. Aikin, Sunil Baidar, W. Alan Brewer, Steven S. Brown, Matthew M. Coggan, Patrick D. Cullis, Jessica Gilman, Georgios I. Gkatzelis, Detlev Helmig, Bryan J. Johnson, K. Emma Knowland, Rajesh Kumar, Aaron D. Lamplugh, Audra McClure-Begley, Brandi J. McCarty, Ann M. Middlebrook, Gabriele Pfister, Jeff Peischl, Irina Petropavlovskikh, Pamela S. Rickley, Andrew W. Rollins, Scott P. Sandberg, Christoph J. Senff, and Carsten Warneke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1938, https://doi.org/10.5194/egusphere-2024-1938, 2024
Preprint withdrawn
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High ozone (O3) formed by reactions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) can harm human health and welfare. High O3 is usually associated with hot summer days, but under certain conditions, high O3 can also form under winter conditions. In this study, we describe a high O3 event that occurred in Colorado during the COVID-19 quarantine that was caused in part by the decrease in traffic, and in part by a shallow inversion created by descent of stratospheric air.
Chandrakala Bharali, Mary Barth, Rajesh Kumar, Sachin D. Ghude, Vinayak Sinha, and Baerbel Sinha
Atmos. Chem. Phys., 24, 6635–6662, https://doi.org/10.5194/acp-24-6635-2024, https://doi.org/10.5194/acp-24-6635-2024, 2024
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This study examines the role of atmospheric aerosols in winter fog over the Indo-Gangetic Plains of India using WRF-Chem. The increase in RH with aerosol–radiation feedback (ARF) is found to be important for fog formation as it promotes the growth of aerosols in the polluted environment. Aqueous-phase chemistry in the fog increases PM2.5 concentration, further affecting ARF. ARF and aqueous-phase chemistry affect the fog intensity and the timing of fog formation by ~1–2 h.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden
Atmos. Meas. Tech., 17, 1941–1963, https://doi.org/10.5194/amt-17-1941-2024, https://doi.org/10.5194/amt-17-1941-2024, 2024
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We assimilate different MOPITT CO products to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately.
Leon Kuhn, Steffen Beirle, Vinod Kumar, Sergey Osipov, Andrea Pozzer, Tim Bösch, Rajesh Kumar, and Thomas Wagner
Atmos. Chem. Phys., 24, 185–217, https://doi.org/10.5194/acp-24-185-2024, https://doi.org/10.5194/acp-24-185-2024, 2024
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NO₂ is an important air pollutant. It was observed that the WRF-Chem model shows significant deviations in NO₂ abundance when compared to measurements. We use a 1-month simulation over central Europe to show that these deviations can be mostly resolved by reparameterization of the vertical mixing routine. In order to validate our results, they are compared to in situ, satellite, and MAX-DOAS measurements.
Miguel Ricardo A. Hilario, Avelino F. Arellano, Ali Behrangi, Ewan C. Crosbie, Joshua P. DiGangi, Glenn S. Diskin, Michael A. Shook, Luke D. Ziemba, and Armin Sorooshian
Atmos. Meas. Tech., 17, 37–55, https://doi.org/10.5194/amt-17-37-2024, https://doi.org/10.5194/amt-17-37-2024, 2024
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Wet scavenging strongly influences aerosol lifetime and interactions but is a large uncertainty in global models. We present a method to identify meteorological variables relevant for estimating wet scavenging. During long-range transport over the tropical western Pacific, relative humidity and the frequency of humid conditions are better predictors of scavenging than precipitation. This method can be applied to other regions, and our findings can inform scavenging parameterizations in models.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Genevieve Rose Lorenzo, Avelino F. Arellano, Maria Obiminda Cambaliza, Christopher Castro, Melliza Templonuevo Cruz, Larry Di Girolamo, Glenn Franco Gacal, Miguel Ricardo A. Hilario, Nofel Lagrosas, Hans Jarett Ong, James Bernard Simpas, Sherdon Niño Uy, and Armin Sorooshian
Atmos. Chem. Phys., 23, 10579–10608, https://doi.org/10.5194/acp-23-10579-2023, https://doi.org/10.5194/acp-23-10579-2023, 2023
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Aerosol and weather interactions in Southeast Asia are complex and understudied. An emerging aerosol climatology was established in Metro Manila, the Philippines, from aerosol particle physicochemical properties and meteorology, revealing five sources. Even with local traffic, transported smoke from biomass burning, aged dust, and cloud processing, background marine particles dominate and correspond to lower aerosol optical depth in Metro Manila compared to other Southeast Asian megacities.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
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A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Matthew S. Johnson, Amir H. Souri, Sajeev Philip, Rajesh Kumar, Aaron Naeger, Jeffrey Geddes, Laura Judd, Scott Janz, Heesung Chong, and John Sullivan
Atmos. Meas. Tech., 16, 2431–2454, https://doi.org/10.5194/amt-16-2431-2023, https://doi.org/10.5194/amt-16-2431-2023, 2023
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Satellites provide vital information for studying the processes controlling ozone formation. Based on the abundance of particular gases in the atmosphere, ozone formation is sensitive to specific human-induced and natural emission sources. However, errors and biases in satellite retrievals hinder this data source’s application for studying ozone formation sensitivity. We conducted a thorough statistical evaluation of two commonly applied satellites for investigating ozone formation sensitivity.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys., 23, 5467–5486, https://doi.org/10.5194/acp-23-5467-2023, https://doi.org/10.5194/acp-23-5467-2023, 2023
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Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire by decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offset some of the fire reduction.
Prajjwal Rawat, Manish Naja, Evan Fishbein, Pradeep K. Thapliyal, Rajesh Kumar, Piyush Bhardwaj, Aditya Jaiswal, Sugriva N. Tiwari, Sethuraman Venkataramani, and Shyam Lal
Atmos. Meas. Tech., 16, 889–909, https://doi.org/10.5194/amt-16-889-2023, https://doi.org/10.5194/amt-16-889-2023, 2023
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Satellite-based ozone observations have gained importance due to their global coverage. However, satellite-retrieved products are indirect and need to be validated, particularly over mountains. Ozonesondes launched from a Himalayan site are used to assess the Atmospheric Infrared Sounder (AIRS) ozone retrieval. AIRS is shown to overestimate ozone in the upper troposphere and lower stratosphere, while the differences from ozonesondes are more minor in the middle troposphere and stratosphere.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Pooja V. Pawar, Sachin D. Ghude, Gaurav Govardhan, Prodip Acharja, Rachana Kulkarni, Rajesh Kumar, Baerbel Sinha, Vinayak Sinha, Chinmay Jena, Preeti Gunwani, Tapan Kumar Adhya, Eiko Nemitz, and Mark A. Sutton
Atmos. Chem. Phys., 23, 41–59, https://doi.org/10.5194/acp-23-41-2023, https://doi.org/10.5194/acp-23-41-2023, 2023
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In this study, for the first time in South Asia we compare simulated ammonia, ammonium, and total ammonia using the WRF-Chem model and MARGA measurements during winter in the Indo-Gangetic Plain region. Since observations show HCl promotes the fraction of high chlorides in Delhi, we added HCl / Cl emissions to the model. We conducted three sensitivity experiments with changes in HCl emissions, and improvements are reported in accurately simulating ammonia, ammonium, and total ammonia.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
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Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
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This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
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Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Fernando Chouza, Thierry Leblanc, Mark Brewer, Patrick Wang, Sabino Piazzolla, Gabriele Pfister, Rajesh Kumar, Carl Drews, Simone Tilmes, Louisa Emmons, and Matthew Johnson
Atmos. Chem. Phys., 21, 6129–6153, https://doi.org/10.5194/acp-21-6129-2021, https://doi.org/10.5194/acp-21-6129-2021, 2021
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The tropospheric ozone lidar at the JPL Table Mountain Facility (TMF) was used to investigate the impact of Los Angeles (LA) Basin pollution transport and stratospheric intrusions in the planetary boundary layer on the San Gabriel Mountains. The results of this study indicate a dominant role of the LA Basin pollution on days when high ozone levels were observed at TMF (March–October period).
Julián Gelman Constantin, Lucas Ruiz, Gustavo Villarosa, Valeria Outes, Facundo N. Bajano, Cenlin He, Hector Bajano, and Laura Dawidowski
The Cryosphere, 14, 4581–4601, https://doi.org/10.5194/tc-14-4581-2020, https://doi.org/10.5194/tc-14-4581-2020, 2020
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We present the results of two field campaigns and modeling activities on the impact of atmospheric particles on Alerce Glacier (Argentinean Andes). We found that volcanic ash remains at different snow layers several years after eruption, increasing light absorption on the glacier surface (with a minor contribution of soot). This leads to 36 % higher annual glacier melting. We find remarkably that volcano eruptions in 2011 and 2015 have a relevant effect on the glacier even in 2016 and 2017.
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Yonghoon Choi, Joshua P. DiGangi, Glenn S. Diskin, Xiaomei Xu, Cenlin He, Helen Worden, Simone Tilmes, Rebecca Buchholz, Hannah S. Halliday, and Avelino F. Arellano
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-864, https://doi.org/10.5194/acp-2020-864, 2020
Revised manuscript not accepted
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A specific demonstration of the potential use of correlative information from carbon monoxide to refine estimates of regional carbon dioxide emissions from fossil fuel combustion.
Chinmay Jena, Sachin D. Ghude, Rachana Kulkarni, Sreyashi Debnath, Rajesh Kumar, Vijay Kumar Soni, Prodip Acharja, Santosh H. Kulkarni, Manoj Khare, Akshara J. Kaginalkar, Dilip M. Chate, Kaushar Ali, Ravi S. Nanjundiah, and Madhavan N. Rajeevan
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-673, https://doi.org/10.5194/acp-2020-673, 2020
Publication in ACP not foreseen
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Simulations of atmospheric particulate matter (PM2.5) with WRF-Chem model with three different aerosol mechanisms coupled with gas-phase chemical schemes are compared to understand the spatial and temporal variability of PM2.5 over the Indo-Gangetic Plain (IGP) in the winter season. All three chemical schemes underestimate the observed concentrations of major aerosol composition and precursor gases over IGP which in turn affect the optical depth and overall performance of the model for PM2.5.
Meng Gao, Jinhui Gao, Bin Zhu, Rajesh Kumar, Xiao Lu, Shaojie Song, Yuzhong Zhang, Beixi Jia, Peng Wang, Gufran Beig, Jianlin Hu, Qi Ying, Hongliang Zhang, Peter Sherman, and Michael B. McElroy
Atmos. Chem. Phys., 20, 4399–4414, https://doi.org/10.5194/acp-20-4399-2020, https://doi.org/10.5194/acp-20-4399-2020, 2020
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A regional fully coupled meteorology–chemistry model, Weather Research and Forecasting model with Chemistry (WRF-Chem), was employed to study the seasonality of ozone (O3) pollution and its sources in both China and India.
Wei Pu, Jiecan Cui, Tenglong Shi, Xuelei Zhang, Cenlin He, and Xin Wang
Atmos. Chem. Phys., 19, 9949–9968, https://doi.org/10.5194/acp-19-9949-2019, https://doi.org/10.5194/acp-19-9949-2019, 2019
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LAPs (light-absorbing particles) deposited on snow can decrease snow albedo and increase the absorption of solar radiation. Radiative forcing by LAPs will affect the regional hydrological cycle and climate. We use MODIS observations to retrieve the radiative forcing by LAPs in snow across northeastern China (NEC). The results of radiative forcing present distinct spatial variability. We find that the biases are negatively correlated with LAP concentrations and range from
~ 5 % to ~ 350 %.
Marianne Tronstad Lund, Gunnar Myhre, Amund Søvde Haslerud, Ragnhild Bieltvedt Skeie, Jan Griesfeller, Stephen Matthew Platt, Rajesh Kumar, Cathrine Lund Myhre, and Michael Schulz
Geosci. Model Dev., 11, 4909–4931, https://doi.org/10.5194/gmd-11-4909-2018, https://doi.org/10.5194/gmd-11-4909-2018, 2018
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Atmospheric aerosols play a key role in the climate system, but their exact impact on the energy balance remains uncertain. Accurate representation of the geographical distribution and properties of aerosols in global models is key to reduce this uncertainty. Here we use a new emission inventory and a range of observations to carefully validate a state-of-the-art model and present an updated estimate of the net direct effect of anthropogenic aerosols since the preindustrial era.
Piyush Bhardwaj, Manish Naja, Maheswar Rupakheti, Aurelia Lupascu, Andrea Mues, Arnico Kumar Panday, Rajesh Kumar, Khadak Singh Mahata, Shyam Lal, Harish C. Chandola, and Mark G. Lawrence
Atmos. Chem. Phys., 18, 11949–11971, https://doi.org/10.5194/acp-18-11949-2018, https://doi.org/10.5194/acp-18-11949-2018, 2018
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This study provides information about the regional variabilities in some of the pollutants using observations in Nepal and India. It is shown that agricultural crop residue burning leads to a significant enhancement in ozone and CO over a wider region. Further, the wintertime higher ozone levels are shown to be largely due to local emissions, while regional transport could be important in spring and hence shows the role of regional sources versus local sources in the Kathmandu Valley.
Cenlin He, Mark G. Flanner, Fei Chen, Michael Barlage, Kuo-Nan Liou, Shichang Kang, Jing Ming, and Yun Qian
Atmos. Chem. Phys., 18, 11507–11527, https://doi.org/10.5194/acp-18-11507-2018, https://doi.org/10.5194/acp-18-11507-2018, 2018
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Snow albedo plays a key role in the Earth and climate system. It can be affected by impurities and snow properties. This study implements new parameterizations into a widely used snow model to account for effects of snow shape and black carbon–snow mixing state on snow albedo reduction in the Tibetan Plateau. This study points toward an imperative need for extensive measurements and improved model characterization of snow grain shape and aerosol–snow mixing state in Tibet and elsewhere.
Yingying Yan, Jintai Lin, and Cenlin He
Atmos. Chem. Phys., 18, 1185–1202, https://doi.org/10.5194/acp-18-1185-2018, https://doi.org/10.5194/acp-18-1185-2018, 2018
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Examining observed and simulated ozone at about 1000 sites during 1990–2014, we find a clear diurnal cycle both in the magnitude of ozone trends and in the relative importance of climate variability versus anthropogenic emissions to ozone changes, which has policy implications to mitigate ozone at night and other non-peak hours.
Bin Zhao, Kuo-Nan Liou, Yu Gu, Jonathan H. Jiang, Qinbin Li, Rong Fu, Lei Huang, Xiaohong Liu, Xiangjun Shi, Hui Su, and Cenlin He
Atmos. Chem. Phys., 18, 1065–1078, https://doi.org/10.5194/acp-18-1065-2018, https://doi.org/10.5194/acp-18-1065-2018, 2018
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The interactions between aerosols and ice clouds represent one of the largest uncertainties among anthropogenic forcings on climate change. We find that the responses of ice crystal effective radius, a key parameter determining ice clouds' net radiative effect, to aerosol loadings are modulated by water vapor amount and vary from a significant negative correlation in moist conditions (consistent with the “Twomey effect” for liquid clouds) to a strong positive correlation in dry conditions.
Youhua Tang, Mariusz Pagowski, Tianfeng Chai, Li Pan, Pius Lee, Barry Baker, Rajesh Kumar, Luca Delle Monache, Daniel Tong, and Hyun-Cheol Kim
Geosci. Model Dev., 10, 4743–4758, https://doi.org/10.5194/gmd-10-4743-2017, https://doi.org/10.5194/gmd-10-4743-2017, 2017
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In order to evaluate the data assimilation tools for regional real-time PM2.5 forecasts, we applied a 3D-Var assimilation tool to adjust the aerosol initial condition by assimilating satellite-retrieved aerosol optical depth and surface PM2.5 observations for a regional air quality model, which is compared to another assimilation method, optimal interpolation. We discuss the pros and cons of these two assimilation methods based on the comparison of their 1-month four-cycles-per-day runs.
Ling Qi, Qinbin Li, Daven K. Henze, Hsien-Liang Tseng, and Cenlin He
Atmos. Chem. Phys., 17, 9697–9716, https://doi.org/10.5194/acp-17-9697-2017, https://doi.org/10.5194/acp-17-9697-2017, 2017
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We find that Asian anthropogenic sources are the largest contributors (~ 40 %) to surface BC in spring in the Arctic, inconsistent with previous studies which repeatedly identified sources of surface BC as anthropogenic emissions from Europe and Russia. It takes 12–17 days for Asian anthropogenic emissions to be transported to the Arctic surface. Additionally, a large fraction (40–65 %) of Asian contribution is in the form of chronic pollution on 1- to 2-month timescales.
Ling Qi, Qinbin Li, Cenlin He, Xin Wang, and Jianping Huang
Atmos. Chem. Phys., 17, 7459–7479, https://doi.org/10.5194/acp-17-7459-2017, https://doi.org/10.5194/acp-17-7459-2017, 2017
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Black carbon (BC) is the second only to CO2 in heating the planet, but the simulation of BC is associated with large uncertainties. BC burden is largely underestimated over land and overestimated over ocean. Our study finds that a missing process in current Wegener–Bergeron–Findeisen models largely explains the discrepancy in BC simulation over land. We call for more observations of BC in mixed-phase clouds to understand this process and improve the simulation of global BC.
Ling Qi, Qinbin Li, Yinrui Li, and Cenlin He
Atmos. Chem. Phys., 17, 1037–1059, https://doi.org/10.5194/acp-17-1037-2017, https://doi.org/10.5194/acp-17-1037-2017, 2017
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The Arctic is the most vulnerable region for climate change. Black carbon (BC) in air and deposited on snow and ice warms the Arctic substantially, but simulations of BC climate effects are associated with large uncertainties. To reduce this uncertainty, it is imperative to improve the simulation of BC distribution in the Arctic. We evaluate the effects of controlling factors (emissions, dry and wet deposition) on BC distribution and call for more observations to constrain these processes.
Bin Zhao, Kuo-Nan Liou, Yu Gu, Cenlin He, Wee-Liang Lee, Xing Chang, Qinbin Li, Shuxiao Wang, Hsien-Liang R. Tseng, Lai-Yung R. Leung, and Jiming Hao
Atmos. Chem. Phys., 16, 5841–5852, https://doi.org/10.5194/acp-16-5841-2016, https://doi.org/10.5194/acp-16-5841-2016, 2016
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We examine the impact of buildings on surface solar fluxes in Beijing by accounting for their 3-D structures. We find that inclusion of buildings changes surface solar fluxes by within ±1 W m−2, ±1–10 W m−2, and up to ±100 W m−2 at grid resolutions of 4 km, 800 m, and 90 m, respectively. We can resolve pairs of positive-negative flux deviations on different sides of buildings at ≤ 800 m resolutions. We should treat building-effect on solar fluxes differently in models with different resolutions.
Cenlin He, Qinbin Li, Kuo-Nan Liou, Ling Qi, Shu Tao, and Joshua P. Schwarz
Atmos. Chem. Phys., 16, 3077–3098, https://doi.org/10.5194/acp-16-3077-2016, https://doi.org/10.5194/acp-16-3077-2016, 2016
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Blarck carbon aging significantly affects its global distribution and thus climatic effects. This study develops a microphysics-based BC aging scheme in a global model, which substantially improves model simulations of BC over the remote Pacific. The microphysical scheme shows fast aging over source regions and much slower aging in remote regions. The microphysical aging significantly reduces global BC burden and lifetime, showing important implications for the estimate of BC radiative effects.
C. He, K.-N. Liou, Y. Takano, R. Zhang, M. Levy Zamora, P. Yang, Q. Li, and L. R. Leung
Atmos. Chem. Phys., 15, 11967–11980, https://doi.org/10.5194/acp-15-11967-2015, https://doi.org/10.5194/acp-15-11967-2015, 2015
Y. H. Mao, Q. B. Li, D. K. Henze, Z. Jiang, D. B. A. Jones, M. Kopacz, C. He, L. Qi, M. Gao, W.-M. Hao, and K.-N. Liou
Atmos. Chem. Phys., 15, 7685–7702, https://doi.org/10.5194/acp-15-7685-2015, https://doi.org/10.5194/acp-15-7685-2015, 2015
R. Kumar, M. C. Barth, V. S. Nair, G. G. Pfister, S. Suresh Babu, S. K. Satheesh, K. Krishna Moorthy, G. R. Carmichael, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 15, 5415–5428, https://doi.org/10.5194/acp-15-5415-2015, https://doi.org/10.5194/acp-15-5415-2015, 2015
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We examine differences in the surface BC between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identify dominant sources of BC in South Asia during ICARB. Anthropogenic emissions were the main source of BC during ICARB and had about 5 times stronger influence on the BoB compared to the AS. Regional-scale transport contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions.
C. He, Q. B. Li, K. N. Liou, J. Zhang, L. Qi, Y. Mao, M. Gao, Z. Lu, D. G. Streets, Q. Zhang, M. M. Sarin, and K. Ram
Atmos. Chem. Phys., 14, 7091–7112, https://doi.org/10.5194/acp-14-7091-2014, https://doi.org/10.5194/acp-14-7091-2014, 2014
R. Kumar, M. C. Barth, S. Madronich, M. Naja, G. R. Carmichael, G. G. Pfister, C. Knote, G. P. Brasseur, N. Ojha, and T. Sarangi
Atmos. Chem. Phys., 14, 6813–6834, https://doi.org/10.5194/acp-14-6813-2014, https://doi.org/10.5194/acp-14-6813-2014, 2014
R. Kumar, M. C. Barth, G. G. Pfister, M. Naja, and G. P. Brasseur
Atmos. Chem. Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, https://doi.org/10.5194/acp-14-2431-2014, 2014
Related subject area
Topics: Atmosphere | Interactions: Human/Earth system interactions | Methods: Machine learning
Constraining uncertainty in projected precipitation over land with causal discovery
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
Earth Syst. Dynam., 16, 607–630, https://doi.org/10.5194/esd-16-607-2025, https://doi.org/10.5194/esd-16-607-2025, 2025
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Projecting future precipitation is essential for preparing for climate change, but current climate models still have large uncertainties, especially over land. This study presents a new method to improve precipitation projections by identifying which models best capture key climate patterns. By giving more weight to models that better represent these patterns, our approach leads to more reliable future precipitation projections over land.
Cited articles
Andreae, M. O. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth-Sci. Rev., 89, 13–41, 2008. a
Ansari, K. and Ramachandran, S.: Optical and physical characteristics of aerosols over Asia: AERONET, MERRA-2 and CAMS, Atmos. Environ., 326, 120470, https://doi.org/10.1016/j.atmosenv.2024.120470, 2024. a
Archer-Nicholls, S., Lowe, D., Schultz, D. M., and McFiggans, G.: Aerosol–radiation–cloud interactions in a regional coupled model: the effects of convective parameterisation and resolution, Atmos. Chem. Phys., 16, 5573–5594, https://doi.org/10.5194/acp-16-5573-2016, 2016. a, b
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential Impacts of a Warming Climate on Water Availability in Snow-Dominated Regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. a
Barthlott, C., Zarboo, A., Matsunobu, T., and Keil, C.: Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes, Atmos. Chem. Phys., 22, 10841–10860, https://doi.org/10.5194/acp-22-10841-2022, 2022. a
Bergstra, J., Komer, B., Eliasmith, C., Yamins, D., and Cox, D. D.: Hyperopt: a Python library for model selection and hyperparameter optimization, Comput. Sci. Discov., 8, 014008, https://doi.org/10.1088/1749-4699/8/1/014008, 2015. a
Bickel, P. J., Götze, F., and van Zwet, W. R.: Resampling Fewer Than n Observations: Gains, Losses, and Remedies for Losses, Springer, New York, NY, https://doi.org/10.1007/978-1-4614-1314-1_17, 2012. a
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. a
Bonekamp, P. N. J., de Kok, R. J., Collier, E., and Immerzeel, W. W.: Contrasting Meteorological Drivers of the Glacier Mass Balance Between the Karakoram and Central Himalaya, Front. Earth Sci., 7, 107, https://doi.org/10.3389/feart.2019.00107, 2019. a
Bonekamp, P. N. J., Wanders, N., van der Wiel, K., Lutz, A. F., and Immerzeel, W. W.: Using large ensemble modelling to derive future changes in mountain specific climate indicators in a 2 and 3 °C warmer world in High Mountain Asia, Int. J. Climatol., 41, E964–E979, 2021. a
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. a, b
Borys, R. D., Lowenthal, D. H., and Mitchell, D. L.: The relationships among cloud microphysics, chemistry, and precipitation rate in cold mountain clouds, Atmos. Environ., 34, 2593–2602, 2000. a
Brown, H., Wang, H., Flanner, M., Liu, X., Singh, B., Zhang, R., Yang, Y., and Wu, M.: Brown carbon fuel and emission source attributions to global snow darkening effect, J. Adv. Model. Earth Sy., 14, e2021MS002768, https://doi.org/10.1029/2021MS002768, 2022. a
Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A.,, Forster, P. M. Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L. A., and Pierce, J. R.: Large contribution of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67–71, 2013. a
Castelvecchi, D.: Can we open the black box of AI?, Nature News, 538, 20, 2016. a
Cerveny, R. S. and Balling Jr., R. C.: The impact of snow cover on diurnal temperature range, Geophys. Res. Lett., 19, 797–800, 1992. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, Association for Computing Machinery, https://doi.org/10.1145/2939672.2939785, 2016. a
Christidis, N. and Stott, P. A.: Changes in the Geopotential Height at 500 hPa under the Influence of External Climatic Forcings, Geophys. Res. Lett., 42, 10798–10806, https://doi.org/10.1002/2015GL066669, 2015. a
Cohen, J. and Entekhabi, D.: The influence of snow cover on northern hemisphere climate variability, Atmosphere-Ocean, 39, 35–53, 2001. a
Copernicus Atmosphere Monitoring Service: CAMS global reanalysis (EAC4), Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store [data set], https://doi.org/10.24381/d58bbf47, 2020. a
Crumley, R. L., Palomaki, R. T., Nolin, A. W., Sproles, E. A., and Mar, E. J.: SnowCloudMetrics: Snow Information for Everyone, Remote Sens., 12, 3341, https://doi.org/10.3390/rs12203341, 2020. a
Dagan, G., Yeheskel, N., and Williams, A. I. L.: Radiative forcing from aerosol–cloud interactions enhanced by large-scale circulation adjustments, Nat. Geosci., 16, 1092–1098, https://doi.org/10.1038/s41561-023-01319-8, 2023. a
Danielson, J. J. and Gesch, D. B.: Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010), U.S. Geological Survey Open-File Report 2011–1073, 26 pp., https://doi.org/10.3133/ofr20111073, 2011. a, b
Das, S., Giorgi, F., Coppola, E., Panicker, A. S., Gautam, A. S., Nair, V. S., and Giuliani, G.: Linkage between the absorbing aerosol-induced snow darkening effects over the Himalayas-Tibetan Plateau and the pre-monsoon climate over northern India, Theor. Appl. Climatol., 147, 1033–1048, 2022. a
Duan, A. and Wu, G.: Change of cloud amount and the climate warming on the Tibetan Plateau, Geophys. Res. Lett., 33, L22704, https://doi.org/10.1029/2006GL027946, 2006. a
Fan, J., Rosenfeld, D., Ding, Y., Leung, L. R., and Li, Z.: Potential aerosol indirect effects on atmospheric circulation and radiative forcing through deep convection, Geophys. Res. Lett., 39, L09806, https://doi.org/10.1029/2012GL051851, 2012. a
Fernandes, R., Zhao, H., Wang, X., Key, J., Qu, X., and Hall, A.: Controls on Northern Hemisphere Snow Albedo Feedback Quantified Using Satellite Earth Observations, Geophys. Res. Lett., 36, L21702, https://doi.org/10.1029/2009GL040057, 2009. a
Flanner, M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J.: Present-day climate forcing and response from black carbon in snow, J. Geophys. Res.-Atmos., 112, D11202, https://doi.org/10.1029/2006JD008003, 2007. a
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.: Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008, Nat. Geosci., 4, 151–155, 2011. a
Flanner, M. G., Arnheim, J. B., Cook, J. M., Dang, C., He, C., Huang, X., Singh, D., Skiles, S. M., Whicker, C. A., and Zender, C. S.: SNICAR-ADv3: a community tool for modeling spectral snow albedo, Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, 2021. a
Gautam, R., Hsu, N. C., Lau, W. K.-M., and Yasunari, T. J.: Satellite observations of desert dust-induced Himalayan snow darkening, Geophys. Res. Lett., 40, 988–993, 2013. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A. Darmenov, A., Bosilovich, M. G. Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, 2017. a
Gettelman, A.: Putting the clouds back in aerosol–cloud interactions, Atmos. Chem. Phys., 15, 12397–12411, https://doi.org/10.5194/acp-15-12397-2015, 2015. a
Gillett, N. P., Fyfe, J. C., and Parker, D. E.: Attribution of Observed Sea Level Pressure Trends to Greenhouse Gas, Aerosol, and Ozone Changes, Geophys. Res. Lett., 40, 2302–2306, https://doi.org/10.1002/grl.50500, 2013. a
Giorgi, F. and Gao, X.-J.: Regional earth system modeling: review and future directions, Atmos. Ocean. Sci. Lett., 11, 189–197, https://doi.org/10.1080/16742834.2018.1452520, 2018. a
Global Modeling and Assimilation Office (GMAO): MERRA-2 inst1_2d_asm_Nx: 2d,1-Hourly,Instantaneous,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/3Z173KIE2TPD, 2015a. a
Global Modeling and Assimilation Office (GMAO): MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/LTVB4GPCOTK2, 2015b. a
Global Modeling and Assimilation Office (GMAO): MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/WWQSXQ8IVFW8, 2015c. a
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_aer_Nx: 2d,1-Hourly,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/KLICLTZ8EM9D, 2015d. a
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/7MCPBJ41Y0K6, 2015e. a
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/RKPHT8KC1Y1T, 2015f. a
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/Q9QMY5PBNV1T, 2015g. a
Gong, Y., Yang, S., Yin, J., Wang, S., Pan, X., Li, D., and Yi, X.: Validation of the Reproducibility of Warm-Season Northeast China Cold Vortices for ERA5 and MERRA-2 Reanalysis, J. Appl. Meteorol. Climatol., 61, 1349–1366, https://doi.org/10.1175/JAMC-D-22-0052.1, 2022. a
Gregory, J. M., Ingram, W. J., Palmer, M. A., Jones, G. S., Stott, P. A., Thorpe, R. B., Lowe, J. A., Johns, T. C., and Williams, K. D.: A New Method for Diagnosing Radiative Forcing and Climate Sensitivity, Geophys. Res. Lett., 31, L03205, https://doi.org/10.1029/2003GL018747, 2004. a
Gryspeerdt, E., Stier, P., White, B. A., and Kipling, Z.: Wet scavenging limits the detection of aerosol effects on precipitation, Atmos. Chem. Phys., 15, 7557–7570, https://doi.org/10.5194/acp-15-7557-2015, 2015. a
Gueymard, C. A. and Yang, D.: Worldwide validation of CAMS and MERRA-2 reanalysis aerosol optical depth products using 15 years of AERONET observations, Atmos. Environ., 225, 117216, https://doi.org/10.1016/j.atmosenv.2019.117216, 2020. a
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG, MYD10C1, Version 61, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MYD10C1.061, 2021a. a, b
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG, MOD10C1, Version 61, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MOD10C1.061, 2021b. a, b
Hao, D., Bisht, G., Gu, Y., Lee, W.-L., Liou, K.-N., and Leung, L. R.: A parameterization of sub-grid topographical effects on solar radiation in the E3SM Land Model (version 1.0): implementation and evaluation over the Tibetan Plateau, Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, 2021. a
Harder, P., Pomeroy, J. W., and Helgason, W.: Local-Scale Advection of Sensible and Latent Heat During Snowmelt, Geophys. Res. Lett., 44, 9769–9777, https://doi.org/10.1002/2017GL074394, 2017. a
He, C.: Modelling light-absorbing particle–snow–radiation interactions and impacts on snow albedo: fundamentals, recent advances and future directions, Environ. Chem., 19, 296–311, 2022. a
He, C., Flanner, M. G., Chen, F., Barlage, M., Liou, K.-N., Kang, S., Ming, J., and Qian, Y.: Black carbon-induced snow albedo reduction over the Tibetan Plateau: uncertainties from snow grain shape and aerosol–snow mixing state based on an updated SNICAR model, Atmos. Chem. Phys., 18, 11507–11527, https://doi.org/10.5194/acp-18-11507-2018, 2018. a
Heinze, C., Eyring, V., Friedlingstein, P., Jones, C., Balkanski, Y., Collins, W., Fichefet, T., Gao, S., Hall, A., Ivanova, D., Knorr, W., Knutti, R., Löw, A., Ponater, M., Schultz, M. G., Schulz, M., Siebesma, P., Teixeira, J., Tselioudis, G., and Vancoppenolle, M.: ESD Reviews: Climate feedbacks in the Earth system and prospects for their evaluation, Earth Syst. Dynam., 10, 379–452, https://doi.org/10.5194/esd-10-379-2019, 2019. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023a. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023b. a
Hileman, B.: Web of interactions makes it difficult to untangle global warming data, Chemical and Engineering News, United States, 70, 1992. a
HiMAT: MATCHA (Model for Atmospheric Transport and Chemistry in Asia), https://himat.org/topic/matcha/, last access: 27 June 2025. a
Holtslag, A. a. M., Svensson, G., Baas, P., Basu, S., Beare, B., Beljaars, A. C. M., Bosveld, F. C., Cuxart, J., Lindvall, J., Steeneveld, G. J., Tjernström, M., and Wiel, B. J. H. V. D.: Stable Atmospheric Boundary Layers and Diurnal Cycles: Challenges for Weather and Climate Models, B. Am. Meteorol. Soc., 94, 1691–1706, https://doi.org/10.1175/BAMS-D-11-00187.1, 2013. a
Hu, Y., Yu, H., Kang, S., Yang, J., Rai, M., Yin, X., Chen, X., and Chen, P.: Aerosol–meteorology feedback diminishes the transboundary transport of black carbon into the Tibetan Plateau, Atmos. Chem. Phys., 24, 85–107, https://doi.org/10.5194/acp-24-85-2024, 2024. a
Huang, H., Qian, Y., He, C., Bair, E. H., and Rittger, K.: Snow Albedo Feedbacks Enhance Snow Impurity-Induced Radiative Forcing in the Sierra Nevada, Geophys. Res. Lett., 49, e2022GL098102, https://doi.org/10.1029/2022GL098102, 2022. a
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J.,, and Tan, J.: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07, edited by: Savtchenko, A., Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2014. a
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07, edited by: Savtchenko, A., Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, https://doi.org/10.5067/GPM/IMERGDF/DAY/07, 2023. a
Inglis, A., Parnell, A., and Hurley, C. B.: Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models, J. Comput. Graph. Stat., 31, 766–778, 2022. a
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019. a, b
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., and Saynisch-Wagner, J.: Towards neural Earth system modelling by integrating artificial intelligence in Earth system science, Nature Machine Intelligence, 3, 667–674, https://doi.org/10.1038/s42256-021-00374-3, 2021. a
Jentsch, H. and Weidinger, J.: Spatio-Temporal Analysis of Valley Wind Systems in the Complex Mountain Topography of the Rolwaling Himal, Nepal, Atmosphere, 13, 1138, https://doi.org/10.3390/atmos13071138, 2022. a
Jiang, R., Tang, W., Wu, X., and Fu, W.: A random forest approach to the detection of epistatic interactions in case-control studies, BMC Bioinformatics, 10, S65, https://doi.org/10.1186/1471-2105-10-S1-S65, 2009. a
Jiang, X., Zhang, T., Tam, C.-Y., Chen, J., Lau, N.-C., Yang, S., and Wang, Z.: Impacts of ENSO and IOD on Snow Depth Over the Tibetan Plateau: Roles of Convections Over the Western North Pacific and Indian Ocean, J. Geophys. Res.-Atmos., 124, 11961–11975, 2019. a
Jin, M. and Dickinson, R. E.: Land surface skin temperature climatology: benefitting from the strengths of satellite observations, Environ. Res. Lett., 5, 044004, https://doi.org/10.1088/1748-9326/5/4/044004, 2010. a
Johnson, J. S., Cui, Z., Lee, L. A., Gosling, J. P., Blyth, A. M., and Carslaw, K. S.: Evaluating uncertainty in convective cloud microphysics using statistical emulation, J. Adv. Model. Earth Sy., 7, 162–187, 2015. a
Kang, S., Zhang, Y., Qian, Y., and Wang, H.: A review of black carbon in snow and ice and its impact on the cryosphere, Earth-Sci. Rev., 210, 103346, https://doi.org/10.1016/j.earscirev.2020.103346, 2020. a
Kapnick, S. B., Delworth, T. L., Ashfaq, M., Malyshev, S., and Milly, P. C. D.: Snowfall less sensitive to warming in Karakoram than in Himalayas due to a unique seasonal cycle, Nat. Geosci., 7, 834–840, 2014. a
Kok, J. F., Ridley, D. A., Zhou, Q., Miller, R. L., Zhao, C., Heald, C. L., Ward, D. S., Albani, S., and Haustein, K.: Smaller desert dust cooling effect estimated from analysis of dust size and abundance, Nat. Geosci., 10, 274–278, https://doi.org/10.1038/ngeo2912, 2017. a
Kraaijenbrink, P. D. A., Stigter, E. E., Yao, T., and Immerzeel, W. W.: Climate change decisive for Asia's snow meltwater supply, Nat. Clim. Change, 11, 591–597, 2021. a
Kraskov, A., Stögbauer, H., and Grassberger, P.: Estimating mutual information, Phys. Rev. E, 69, 066138, https://doi.org/10.1103/PhysRevE.69.066138, 2004. a
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a
Kumar, R., Barth, M. C., Pfister, G. G., Naja, M., and Brasseur, G. P.: WRF-Chem simulations of a typical pre-monsoon dust storm in northern India: influences on aerosol optical properties and radiation budget, Atmos. Chem. Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, 2014. a
Kumar, R., He, C., Roychoudhury, C., Cheng, W., Mizukami, N., and Arellano, A. F.: High Mountain Asia 12 km Modeled Estimates of Aerosol Transport, Chemistry, and Deposition Reanalysis, 2003–2019 (HMA2_MATCHA, Version 1), Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/CG4OT8DJX2Z7, 2024. a, b
Kumar, R., Naja, M., Satheesh, S. K., Ojha, N., Joshi, H., Sarangi, T., Pant, P., Dumka, U. C., Hegde, P.,and Venkataramani, S.: Influences of the springtime northern Indian biomass burning over the central Himalayas, J. Geophys. Res.-Atmos., 116, D19302, https://doi.org/10.1029/2010JD015509, 2011. a
Kump, L., Kasting, J., and Crane, R.: The Earth System, Prentice Hall, ISBN 9780321597793, 2010. a
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P.: Learning skillful medium-range global weather forecasting, Science, 382, 1416–1421, https://doi.org/10.1126/science.adi2336, 2023. a
Lau, K. M., Kim, M. K., and Kim, K. M.: Asian summer monsoon anomalies induced by aerosol direct forcing: the role of the Tibetan Plateau, Clim. Dynam., 26, 855–864, 2006. a
Lau, W. K. M. and Kim, K.-M.: Impact of Snow Darkening by Deposition of Light-Absorbing Aerosols on Snow Cover in the Himalayas–Tibetan Plateau and Influence on the Asian Summer Monsoon: A Possible Mechanism for the Blanford Hypothesis, Atmosphere, 9, 438, https://doi.org/10.3390/atmos9110438, 2018. a, b, c
Lauer, A., Eyring, V., Bellprat, O., Bock, L., Gier, B. K., Hunter, A., Lorenz, R., Pérez-Zanón, N., Righi, M., Schlund, M., Senftleben, D., Weigel, K., and Zechlau, S.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP, Geosci. Model Dev., 13, 4205–4228, https://doi.org/10.5194/gmd-13-4205-2020, 2020. a
Lee, J., Gleckler, P. J., Ahn, M.-S., Ordonez, A., Ullrich, P. A., Sperber, K. R., Taylor, K. E., Planton, Y. Y., Guilyardi, E., Durack, P., Bonfils, C., Zelinka, M. D., Chao, L.-W., Dong, B., Doutriaux, C., Zhang, C., Vo, T., Boutte, J., Wehner, M. F., Pendergrass, A. G., Kim, D., Xue, Z., Wittenberg, A. T., and Krasting, J.: Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3, Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, 2024. a
Lee, L. A., Pringle, K. J., Reddington, C. L., Mann, G. W., Stier, P., Spracklen, D. V., Pierce, J. R., and Carslaw, K. S.: The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei, Atmos. Chem. Phys., 13, 8879–8914, https://doi.org/10.5194/acp-13-8879-2013, 2013. a
Li, W., Wang, Y., Yi, Z., Guo, B., Chen, W., Che, H., and Zhang, X.: Evaluation of MERRA-2 and CAMS reanalysis for black carbon aerosol in China, Environ. Pollut., 343, 123182, https://doi.org/10.1016/j.envpol.2023.123182, 2024. a
Li, Z., Lau, W. K.-M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M. G., Liu, J., Qian, Y., Li, J., Zhou, T., Fan, J., Rosenfeld, D., Ming, Y., Wang, Y., Huang, J., Wang, B., Xu, X., Lee, S.-S., Cribb, M., Zhang, F., Yang, X., Zhao, C., Takemura, T., Wang, K., Xia, X., Yin, Y., Zhang, H., Guo, J., Zhai, P. M., Sugimoto, N., Babu, S. S., and Brasseur, G. P.: Aerosol and monsoon climate interactions over Asia, Rev. Geophys., 54, 866–929, https://doi.org/10.1002/2015RG000500, 2016. a
Lipton, Z. C.: The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery, Queue, 16, 31–57, 2018. a
Liu, Y. and Margulis, S. A.: High mountain asia UCLA daily snow reanalysis, version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/HNAUGJQXSCVU, 2021. a
Liu, Y., Fang, Y., and Margulis, S. A.: Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset, The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, 2021a. a
Liu, Y., Li, Z., and Chen, Y.: Continuous warming shift greening towards browning in the Southeast and Northwest High Mountain Asia, Sci. Rep., 11, 17920, https://doi.org/10.1038/s41598-021-97240-4, 2021b. a
Liu, Y., Li, Z., Chen, Y., Kayumba, P. M., Wang, X., Liu, C., Long, Y., and Sun, F.: Biophysical impacts of vegetation dynamics largely contribute to climate mitigation in High Mountain Asia, Agr. For. Meteorol., 327, 109233, https://doi.org/10.1016/j.agrformet.2022.109233, 2022. a
Lohmann, U.: Anthropogenic Aerosol Influences on Mixed-Phase Clouds, Current Climate Change Reports, 3, 32–44, 2017. a
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005. a
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.: From local explanations to global understanding with explainable AI for trees, Nat. Mach. Intell., 2, 56–67, 2020. a
Luo, H., Ge, F., Yang, K., Zhu, S., Peng, T., Cai, W., Liu, X., and Tang, W.: Assessment of ECMWF reanalysis data in complex terrain: Can the CERA-20C and ERA-Interim data sets replicate the variation in surface air temperatures over Sichuan, China?, Int. J. Climatol., 39, 5619–5634, https://doi.org/10.1002/joc.6175, 2019. a
Lyapustin, A.: MODIS/Terra+Aqua AOD and Water Vapor from MAIAC, Daily L3 Global 0.05Deg CMG V061, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD19A2CMG.061, 2023. a, b
Maina, F. Z., Kumar, S. V., Albergel, C., and Mahanama, S. P.: Warming, increase in precipitation, and irrigation enhance greening in High Mountain Asia, Commun. Earth Environ., 3, 1–8, 2022. a
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C.-B., Watterson, I. G., Weaver, A. J., and Zhao, Z. C.: Global climate projections, Chap. 10, 748–845, http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter10.pdf (last access: 27 July 2025), 2007. a
Michibata, T., Suzuki, K., and Takemura, T.: Snow-induced buffering in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 13771–13780, https://doi.org/10.5194/acp-20-13771-2020, 2020. a, b
Ming, Y. and Ramaswamy, V.: A Model Investigation of Aerosol-Induced Changes in Tropical Circulation, J. Climate, 24, 5125–5133, https://doi.org/10.1175/2011JCLI4108.1, 2011. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, 1997. a
Moch, J. M., Mickley, L. J., Keller, C. A., Bian, H., Lundgren, E. W., Zhai, S., and Jacob, D. J.: Aerosol-Radiation Interactions in China in Winter: Competing Effects of Reduced Shortwave Radiation and Cloud-Snowfall-Albedo Feedbacks Under Rapidly Changing Emissions, J. Geophys. Res.-Atmos., 127, e2021JD035442, https://doi.org/10.1029/2021JD035442, 2022. a
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.: Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble, The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, 2020. a
Mülmenstädt, J. and Wilcox, L. J.: The Fall and Rise of the Global Climate Model, J. Adv. Model. Earth Sy., 13, e2021MS002781, https://doi.org/10.1029/2021MS002781, 2021. a, b
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
National Academies of Sciences, Engineering, and Medicine: Next Generation Earth Systems Science at the National Science Foundation, The National Academies Press, Washington, DC, https://doi.org/10.17226/26042, 2022. a
National Academies Press: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, National Academies Press, Washington, D.C., https://doi.org/10.17226/24938, 2018. a
Newman, M. E. J.: Assortative Mixing in Networks, Phys. Rev. Lett., 89, 208701, https://doi.org/10.1103/PhysRevLett.89.208701, 2002. a, b, c
NOAA Science Advisory Board: A Report on Priorities for Weather Research, https://sab.noaa.gov/wp-content/uploads/2021/12/PWR-Report_Final_12-9-21.pdf (last access: 27 July 2025), 119, 2021. a
Oaida, C. M., Xue, Y., Flanner, M. G., Skiles, S. M., De Sales, F., and Painter, T. H.: Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S., J. Geophys. Res.-Atmos., 120, 3228–3248, https://doi.org/10.1002/2014JD022444, 2015. a
Ohmura, A., Kasser, P., and Funk, M.: Climate at the Equilibrium Line of Glaciers, J. Glaciol., 38, 397–411, 1992. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E., Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stöckli, R., Wang, A., Yang, Z., Zeng, X., and Zeng, X.: Technical description of version 4.0 of the community land model (CLM), NCAR Tech. Note NCAR/TN-478+ STR, 257, 1–257, https://opensky.ucar.edu/system/files/2024-08/technotes_493.pdf (last access: 27 July 2025), 2010. a
Orsolini, Y., Wegmann, M., Dutra, E., Liu, B., Balsamo, G., Yang, K., de Rosnay, P., Zhu, C., Wang, W., Senan, R., and Arduini, G.: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations, The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, 2019. a
Painter, T. H., Barrett, A. P., Landry, C. C., Neff, J. C., Cassidy, M. P., Lawrence, C. R., McBride, K. E., and Farmer, G. L.: Impact of disturbed desert soils on duration of mountain snow cover, Geophys. Res. Lett., 34, L12502, https://doi.org/10.1029/2007GL030284, 2007. a
Pathak, R., Dasari, H. P., Ashok, K., and Hoteit, I.: Effects of multi-observations uncertainty and models similarity on climate change projections, npj Climate and Atmospheric Science, 6, 1–12, https://doi.org/10.1038/s41612-023-00473-5, 2023. a, b
Pepin, N., Bradley, R. S., Diaz, H. F., Baraer, M., Caceres, E. B., Forsythe, N., Fowler, H., Greenwood, G., Hashmi, M. Z., Liu, X. D., Miller, J. R., Ning, L., Ohmura, A., Palazzi, E., Rangwala, I., Schoener, W., Severskiy, I., Shahgedanova, M., Wang, M. B., Williamson, S. N., and Yang, D. Q.: Elevation-dependent warming in mountain regions of the world, Nat. Clim. Change, 5, 424–430, 2015. a, b, c
Pepin, N. C. and Seidel, D. J.: A global comparison of surface and free-air temperatures at high elevations, J. Geophys. Res.-Atmos., 110, D03104, https://doi.org/10.1029/2004JD005047, 2005. a
Pu, B. and Ginoux, P.: How reliable are CMIP5 models in simulating dust optical depth?, Atmos. Chem. Phys., 18, 12491–12510, https://doi.org/10.5194/acp-18-12491-2018, 2018. a
Qian, Y., Yasunari, T. J., Doherty, S. J., Flanner, M. G., Lau, W. K. M., Ming, J., Wang, H., Wang, M., Warren, S. G., and Zhang, R.: Light-absorbing particles in snow and ice: Measurement and modeling of climatic and hydrological impact, Adv. Atmos. Sci., 32, 64–91, 2015. a
Qu, X. and Hall, A.: What Controls the Strength of Snow-Albedo Feedback?, J. Climate, 20, 3971–3981, https://doi.org/10.1175/JCLI4186.1, 2007. a
Ragettli, S., Immerzeel, W. W., and Pellicciotti, F.: Contrasting climate change impact on river flows from high-altitude catchments in the Himalayan and Andes Mountains, P. Natl. Acad. Sci. USA, 113, 9222–9227, 2016. a
Rahimi, S., Liu, X., Zhao, C., Lu, Z., and Lebo, Z. J.: Examining the atmospheric radiative and snow-darkening effects of black carbon and dust across the Rocky Mountains of the United States using WRF-Chem, Atmos. Chem. Phys., 20, 10911–10935, https://doi.org/10.5194/acp-20-10911-2020, 2020. a
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols, Climate, and the Hydrological Cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001. a
Ramanathan, V., Li, F., Ramana, M. V., Praveen, P. S., Kim, D., Corrigan, C. E., Nguyen, H., Stone, E. A., Schauer, J. J., Carmichael, G. R., Adhikary, B., and Yoon, S. C.: Atmospheric brown clouds: Hemispherical and regional variations in long-range transport, absorption, and radiative forcing, J. Geophys. Res.-Atmos., 112, D22S21, https://doi.org/10.1029/2006JD008124, 2007. a
Randall, D. A., Bitz, C. M., Danabasoglu, G., Denning, A. S., Gent, P. R., Gettelman, A., Griffies, S. M., Lynch, P., Morrison, H., Pincus, R., and Thuburn, J.: 100 Years of Earth System Model Development, Meteorological Monographs, 59, 12.1–12.66, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0018.1, 2018. a
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation, J. Climate, 30, 6823–6850, 2017. a, b
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep Learning and Process Understanding for Data-Driven Earth System Science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines (NSIDC-0770, Version 6), National Snow and Ice Data Center [data set], Boulder, Colorado USA, https://doi.org/10.7265/4m1f-gd79, 2017. a
Rittger, K., Bormann, K. J., Bair, E. H., Dozier, J., and Painter, T. H.: Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI, Front. Remote Sens., 2, 647154, https://doi.org/10.3389/frsen.2021.647154, 2021. a
Robock, A.: Ice and Snow Feedbacks and the Latitudinal and Seasonal Distribution of Climate Sensitivity, J. Atmos. Sci., 40, 986–997, 1983. a
Roychoudhury, C.: Aerosol-Meteorology-Snow-HMA, GitHub, https://github.com/chayanroyc/Aerosol-Meteorology-Snow-
HMA, last access: 27 July 2025. a
Sakai, A. and Fujita, K.: Contrasting glacier responses to recent climate change in high-mountain Asia, Sci. Rep., 7, 13717, https://doi.org/Please provide DOI., 2017. a
Sand, M., Samset, B. H., Tsigaridis, K., Bauer, S. E., and Myhre, G.: Black Carbon and Precipitation: An Energetics Perspective, J. Geophys. Res.-Atmos., 125, e2019JD032239, https://doi.org/10.1029/2019JD032239, 2020. a
Sand, M., Samset, B. H., Myhre, G., Gliß, J., Bauer, S. E., Bian, H., Chin, M., Checa-Garcia, R., Ginoux, P., Kipling, Z., Kirkevåg, A., Kokkola, H., Le Sager, P., Lund, M. T., Matsui, H., van Noije, T., Olivié, D. J. L., Remy, S., Schulz, M., Stier, P., Stjern, C. W., Takemura, T., Tsigaridis, K., Tsyro, S. G., and Watson-Parris, D.: Aerosol absorption in global models from AeroCom phase III, Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, 2021. a
Sandu, I., van Niekerk, A., Shepherd, T. G., Vosper, S. B., Zadra, A., Bacmeister, J., Beljaars, A., Brown, A. R., Dörnbrack, A., McFarlane, N., Pithan, F., and Svensson, G.: Impacts of orography on large-scale atmospheric circulation, npj Climate and Atmospheric Science, 2, 1–8, https://doi.org/10.1038/s41612-019-0065-9, 2019. a
Sarangi, C., Qian, Y., Rittger, K., Rittger, K., Ruby Leung, L., Chand, D., Bormann, K. J., and Painter, T. H.: Dust dominates high-altitude snow darkening and melt over high-mountain Asia, Nat. Clim. Change, 10, 1045–1051, 2020. a
Schlögl, S., Lehning, M., and Mott, R.: How Are Turbulent Sensible Heat Fluxes and Snow Melt Rates Affected by a Changing Snow Cover Fraction?, Front. Earth Sci., 6, 154, https://doi.org/10.3389/feart.2018.00154, 2018. a
Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations, Geophys. Res. Lett., 44, 12396–12417, https://doi.org/10.1002/2017GL076101, 2017. a, b
Senf, F., Quaas, J., and Tegen, I.: Absorbing aerosol decreases cloud cover in cloud-resolving simulations over Germany, Q. J. Roy. Meteor. Soc., 147, 4083–4100, https://doi.org/10.1002/qj.4169, 2021. a
Shepherd, T. G.: Atmospheric circulation as a source of uncertainty in climate change projections, Nat. Geosci., 7, 703–708, https://doi.org/10.1038/ngeo2253, 2014. a
Shi, X., Groisman, P. Y., Déry, S. J., and Lettenmaier, D. P.: The role of surface energy fluxes in pan-Arctic snow cover changes, Environ. Res. Lett., 6, 035204, https://doi.org/10.1088/1748-9326/6/3/035204, 2011. a
Shi, X., Déry, S. J., Groisman, P. Y., and Lettenmaier, D. P.: Relationships between Recent Pan-Arctic Snow Cover and Hydroclimate Trends, J. Climate, 26, 2048–2064, 2013. a
Shi, Z., Xie, X., Li, X., Yang, L., Xie, X., Lei, J., Sha, Y., and Liu, X.: Snow-darkening versus direct radiative effects of mineral dust aerosol on the Indian summer monsoon onset: role of temperature change over dust sources, Atmos. Chem. Phys., 19, 1605–1622, https://doi.org/10.5194/acp-19-1605-2019, 2019. a
Shin, N.-Y., Ham, Y.-G., Kim, J.-H., Cho, M., and Kug, J.-S.: Application of Deep Learning to Understanding ENSO Dynamics, Artif. Intell. Earth Syst., 1, e210011, https://doi.org/10.1175/AIES-D-21-0011.1, 2022. a
Shindell, D. and Faluvegi, G.: Climate response to regional radiative forcing during the twentieth century, Nat. Geosci., 2, 294–300, 2009. a
Singh, D., Zhu, Y., Liu, S., Srivastava, P. K., Dharpure, J. K., Chatterjee, D., Sahu, R., and Gagnon, A. S.: Exploring the Links between Variations in Snow Cover Area and Climatic Variables in a Himalayan Catchment Using Earth Observations and CMIP6 Climate Change Scenarios, J. Hydrol., 608, 127648, https://doi.org/10.1016/j.jhydrol.2022.127648, 2022. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Version 3, NCAR Tech. Note., https://doi.org/10.5065/D68S4MVH, 2008. a
Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., and Shields, C. A.: Quantifying Climate Feedbacks Using Radiative Kernels, J. Climate, 21, 3504–3520, https://doi.org/10.1175/2007JCLI2110.1, 2008. a
Södergren, A. H., McDonald, A. J., and Bodeker, G. E.: An energy balance model exploration of the impacts of interactions between surface albedo, cloud cover and water vapor on polar amplification, Clim. Dynam., 51, 1639–1658, 2018. a
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame, D. J., Kettleborough, J. A., Knight, S., Martin, A., Murphy, J. M., Piani, C., Sexton, D., Smith, L. A., Spicer, R. A., Thorpe, A. J., and Allen, M. R.: Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, 403–406, https://doi.org/10.1038/nature03301, 2005. a
Stante, F., Ermida, S. L., DaCamara, C. C., Göttsche, F.-M., and Trigo, I. F.: Impact of High Concentrations of Saharan Dust Aerosols on Infrared-Based Land Surface Temperature Products, IEEE J. Sel. Top. Appl. Earth Obs., 16, 4064–4079, https://doi.org/10.1109/JSTARS.2023.3263374, 2023. a
Steffen, W., Richardson, K., Rockström, J., Schellnhuber, H. J., Dube, O. P., Dutreuil, S., Lenton, T. M., and Lubchenco, J.: The emergence and evolution of Earth System Science, Nat. Rev. Earth Environ., 1, 54–63, https://doi.org/10.1038/s43017-019-0005-6, 2020. a
Stein, U. and Alpert, P.: Factor Separation in Numerical Simulations, J. Atmos. Sci., 50, 2107–2115, 1993. a
Stevens, B. and Bony, S.: What Are Climate Models Missing?, Science, 340, 1053–1054, https://doi.org/10.1126/science.1237554, 2013. a
Sun, B.: Seasonal evolution of the dominant modes of the Eurasian snowpack and atmospheric circulation from autumn to the subsequent spring and the associated surface heat budget, Atmos. Ocean. Sci. Lett., 10, 191–197, https://doi.org/10.1080/16742834.2017.1286226, 2017. a
Tang, C., Tao, X., Wei, Y., Tong, Z., Zhu, F., and Lin, H.: Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data, Sustainability, 14, 12089, https://doi.org/10.3390/su141912089, 2022. a
Tonidandel, S. and LeBreton, J. M.: Relative importance analysis: A useful supplement to regression analysis, J. Bus. Psychol., 26, 1–9, 2011. a
Tuccella, P., Pitari, G., Colaiuda, V., Raparelli, E., and Curci, G.: Present-day radiative effect from radiation-absorbing aerosols in snow, Atmos. Chem. Phys., 21, 6875–6893, https://doi.org/10.5194/acp-21-6875-2021, 2021. a
UNEP: A Scientific Assessment of the Third Pole Environment, Technical report, United Nations Environment Programme, Nairobi, Kenya, 2022. a
van Vuuren, D. P., Bayer, L. B., Chuwah, C., Ganzeveld, L., Hazeleger, W., van den Hurk, B., van Noije, T., O'Neill, B., and Strengers, B. J.: A Comprehensive View on Climate Change: Coupling of Earth System and Integrated Assessment Models, Environ. Res. Lett., 7, 024012, https://doi.org/10.1088/1748-9326/7/2/024012, 2012. a
Wall, C. J., Norris, J. R., Possner, A., McCoy, D. T., McCoy, I. L., and Lutsko, N. J.: Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean, P. Natl. Acad. Sci. USA, 119, e2210481119, https://doi.org/10.1073/pnas.2210481119, 2022. a
Wan, Z., Hook, S., and Hulley, G.: MOD11C1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD11C1.006, 2015. a, b
Wan, Z., Hook, S., and Hulley, G.: MYD11C1 MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MYD11C1.006, 2015b. a
Wang, P., Yang, Y., Xue, D., Ren, L., Tang, J., Leung, L. R., and Liao, H.: Aerosols Overtake Greenhouse Gases Causing a Warmer Climate and More Weather Extremes toward Carbon Neutrality, Nat. Commun., 14, 7257, https://doi.org/10.1038/s41467-023-42891-2, 2023. a
Wang, W., Huang, X., Deng, J., Xie, H., and Liang, T.: Spatio-Temporal Change of Snow Cover and Its Response to Climate over the Tibetan Plateau Based on an Improved Daily Cloud-Free Snow Cover Product, Remote Sens., 7, 169–194, 2015. a
Warren, S. G. and Wiscombe, W. J.: A Model for the Spectral Albedo of Snow. II: Snow Containing Atmospheric Aerosols, J. Atmos. Sci., 37, 2734–2745, 1980. a
Wei, Y., Peng, K., Ma, Y., Sun, Y., Zhao, D., Ren, X., Yang, S., Ahmad, M., Pan, X., Wang, Z., and Xin, J.: Validation of ERA5 Boundary Layer Meteorological Variables by Remote-Sensing Measurements in the Southeast China Mountains, Remote Sens., 16, 548, https://doi.org/10.3390/rs16030548, 2024. a
Wood Jr., F. B.: The Need for Systems Research on Global Climate Change, Systems Res., 5, 225–240, https://doi.org/10.1002/sres.3850050305, 1988. a
World Meteorological Organization (WMO): The 2022 GCOS ECVs Requirements (GCOS 245), GCOS Rep. 245, 12 pp., https://library.wmo.int/records/item/58111-the-2022-gcos-ecvs-requirements-gcos-245 (last access: 27 July 2025), 2022. a
Wu, L., Gu, Y., Jiang, J. H., Su, H., Yu, N., Zhao, C., Qian, Y., Zhao, B., Liou, K.-N., and Choi, Y.-S.: Impacts of aerosols on seasonal precipitation and snowpack in California based on convection-permitting WRF-Chem simulations, Atmos. Chem. Phys., 18, 5529–5547, https://doi.org/10.5194/acp-18-5529-2018, 2018. a
Wu, X., Naegeli, K., Premier, V., Marin, C., Ma, D., Wang, J., and Wunderle, S.: Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas, The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, 2021. a
Xian, P., Reid, J. S., Ades, M., Benedetti, A., Colarco, P. R., da Silva, A., Eck, T. F., Flemming, J., Hyer, E. J., Kipling, Z., Rémy, S., Sekiyama, T. T., Tanaka, T., Yumimoto, K., and Zhang, J.: Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus, Atmos. Chem. Phys., 24, 6385–6411, https://doi.org/10.5194/acp-24-6385-2024, 2024. a
Xiao, Y., Ke, C.-Q., Shen, X., Cai, Y., and Li, H.: What drives the decrease of glacier surface albedo in High Mountain Asia in the past two decades?, Sci. Total Environ., 863, 160945, https://doi.org/10.1016/j.scitotenv.2022.160945, 2023. a
Yao, T., Xue, Y., Chen, D., Chen, F., Thompson, L., Cui, P., Koike, T., Lau, W. K.-M., Lettenmaier, D., Mosbrugger, V., Zhang, R., Xu, B., Dozier, J., Gillespie, T., Gu, Y., Kang, S., Piao, S., Sugimoto, S., Ueno, K., Wang, L., Wang, W., Zhang, F., Sheng, Y., Guo, W., Ailikun, Yang, X., Ma, Y., Shen, S. S. P., Su, Z., Chen, F., Liang, S., Liu, Y., Singh, V. P., Yang, K., Yang, D., Zhao, X., Qian, Y., Zhang, Y., and Li, Q.: Recent Third Pole's Rapid Warming Accompanies Cryospheric Melt and Water Cycle Intensification and Interactions between Monsoon and Environment: Multidisciplinary Approach with Observations, Modeling, and Analysis, B. Am. Meteorol. Soc., 100, 423–444, https://doi.org/10.1175/BAMS-D-17-0057.1, 2019. a
You, Q., Wu, T., Shen, L., Pepin, N., Zhang, L., Jiang, Z., Wu, Z., Kang, S., and AghaKouchak, A.: Review of snow cover variation over the Tibetan Plateau and its influence on the broad climate system, Earth-Sci. Rev., 201, 103043, https://doi.org/10.1016/j.earscirev.2019.103043, 2020. a
Zhang, F., Thapa, S., Immerzeel, W., Zhang, H., and Lutz, A.: Water availability on the Third Pole: A review, Water Security, 7, 100033, https://doi.org/10.1016/j.wasec.2019.100033, 2019. a
Zhao, A., Ryder, C. L., and Wilcox, L. J.: How well do the CMIP6 models simulate dust aerosols?, Atmos. Chem. Phys., 22, 2095–2119, https://doi.org/10.5194/acp-22-2095-2022, 2022. a, b
Zhao, A., Wilcox, L. J., and Ryder, C. L.: The key role of atmospheric absorption in the Asian summer monsoon response to dust emissions in CMIP6 models, Atmos. Chem. Phys., 24, 13385–13402, https://doi.org/10.5194/acp-24-13385-2024, 2024. a
Zhou, M., Zhang, L., Chen, D., Gu, Y., Fu, T.-M., Gao, M., Zhao, Y., Lu, X., and Zhao, B.: The impact of aerosol–radiation interactions on the effectiveness of emission control measures, Environ. Res. Lett., 14, 024002, https://doi.org/10.1088/1748-9326/aaf27d, 2019. a
Short summary
We present a novel data-driven approach to understand how pollution and weather processes interact to influence snowmelt in Asian glaciers and how these interactions are represented in three climate models. Our findings show where models need improvement in predicting snowmelt, particularly dust and its transport. This method can support future model development for reliable predictions in climate-vulnerable regions.
We present a novel data-driven approach to understand how pollution and weather processes...
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