170 citations as recorded by crossref.

1. Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. https://doi.org/10.5194/gmd-17-2387-2024
2. The impact of performance filtering on climate feedbacks in a perturbed parameter ensemble J. Rostron et al. https://doi.org/10.1007/s00382-020-05281-8
3. Uncertainty in the magnitude of aerosol‐cloud radiative forcing over recent decades L. Regayre et al. https://doi.org/10.1002/2014GL062029
4. Uncertainty in mapped geological boundaries held by a national geological survey:eliciting the geologists' tacit error model R. Lark et al. https://doi.org/10.5194/se-6-727-2015
5. Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model R. Morales Betancourt & A. Nenes https://doi.org/10.5194/acp-14-4809-2014
6. Effect of aerosol radiative forcing uncertainty on projected exceedance year of a 1.5 °C global temperature rise A. Peace et al. https://doi.org/10.1088/1748-9326/aba20c
7. Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models H. Wan et al. https://doi.org/10.5194/gmd-7-1961-2014
8. Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and Their Radiative Forcing M. Yoshioka et al. https://doi.org/10.1029/2019MS001628
9. Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability S. Ghan et al. https://doi.org/10.1073/pnas.1514036113
10. A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the community atmosphere model CAM5 C. Zhao et al. https://doi.org/10.5194/acp-13-10969-2013
11. The contribution of plume-scale nucleation to global and regional aerosol and CCN concentrations: evaluation and sensitivity to emissions changes R. Stevens & J. Pierce https://doi.org/10.5194/acp-14-13661-2014
12. Enhanced New Particle Formation Above the Marine Boundary Layer Over the Yellow Sea: Potential Impacts on Cloud Condensation Nuclei N. Takegawa et al. https://doi.org/10.1029/2019JD031448
13. Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations A. Wilson et al. https://doi.org/10.1137/20M1318560
14. Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Models D. Williamson & M. Goldstein https://doi.org/10.1214/15-BA966SI
15. The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation M. Righi et al. https://doi.org/10.5194/acp-16-4481-2016
16. Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation R. Sullivan et al. https://doi.org/10.1002/2016JD025568
17. Impact of anomalous forest fire on aerosol radiative forcing and snow cover over Himalayan region K. Bali et al. https://doi.org/10.1016/j.atmosenv.2016.11.061
18. The cause of solar dimming and brightening at the Earth's surface during the last half century: Evidence from measurements of sunshine duration G. Stanhill et al. https://doi.org/10.1002/2013JD021308
19. On the composition of ammonia–sulfuric-acid ion clusters during aerosol particle formation S. Schobesberger et al. https://doi.org/10.5194/acp-15-55-2015
20. Using Emulators to Understand the Sensitivity of Deep Convective Clouds and Hail to Environmental Conditions C. Wellmann et al. https://doi.org/10.1029/2018MS001465
21. The CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017) measurement campaign J. Haywood et al. https://doi.org/10.5194/acp-21-1049-2021
22. Inverse modelling of Köhler theory – Part 1: A response surface analysis of CCN spectra with respect to surface-active organic species S. Lowe et al. https://doi.org/10.5194/acp-16-10941-2016
23. Assimilation of POLDER observations to estimate aerosol emissions A. Tsikerdekis et al. https://doi.org/10.5194/acp-23-9495-2023
24. Understanding global secondary organic aerosol amount and size-resolved condensational behavior S. D'Andrea et al. https://doi.org/10.5194/acp-13-11519-2013
25. ORACLE (v1.0): module to simulate the organic aerosol composition and evolution in the atmosphere A. Tsimpidi et al. https://doi.org/10.5194/gmd-7-3153-2014
26. Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones F. He et al. https://doi.org/10.1175/MWR-D-17-0265.1
27. The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing J. Johnson et al. https://doi.org/10.5194/acp-18-13031-2018
28. Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3 Y. Bhatti et al. https://doi.org/10.5194/acp-26-269-2026
29. Deep learning representation of the aerosol size distribution D. Barahona et al. https://doi.org/10.5194/gmd-19-2437-2026
30. SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra J. Verrelst et al. https://doi.org/10.3390/rs9090927
31. Large-scale evaluation of WRF-chem model and dry deposition schemes during a Saharan dust event over the Iberian Peninsula R. Silva et al. https://doi.org/10.1016/j.atmosenv.2025.121293
32. Resolving aerosol mixing state increases accuracy of black carbon respiratory deposition estimates J. Ching et al. https://doi.org/10.1016/j.oneear.2020.11.004
33. Non-Stokes deposition velocities observed under Rayleigh–Bénard turbulence K. Swartz-Schult et al. https://doi.org/10.1080/02786826.2026.2631799
34. Formation and growth of nucleated particles into cloud condensation nuclei: model–measurement comparison D. Westervelt et al. https://doi.org/10.5194/acp-13-7645-2013
35. Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity O. Wild et al. https://doi.org/10.5194/acp-20-4047-2020
36. Reevaluating the contribution of sulfuric acid and the origin of organic compounds in atmospheric nanoparticle growth V. Vakkari et al. https://doi.org/10.1002/2015GL066459
37. Processes controlling the vertical aerosol distribution in marine stratocumulus regions – a sensitivity study using the climate model NorESM1-M L. Frey et al. https://doi.org/10.5194/acp-21-577-2021
38. MATCH-SALSA – Multi-scale Atmospheric Transport and CHemistry model coupled to the SALSA aerosol microphysics model – Part 1: Model description and evaluation C. Andersson et al. https://doi.org/10.5194/gmd-8-171-2015
39. Black carbon simulations using a size‐ and mixing‐state‐resolved three‐dimensional model: 1. Radiative effects and their uncertainties H. Matsui https://doi.org/10.1002/2015JD023998
40. Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF L. Regayre et al. https://doi.org/10.5194/acp-18-9975-2018
41. Black Carbon Absorption Efficiency Under Preindustrial and Present‐Day Conditions Simulated by a Size‐ and Mixing‐State‐Resolved Global Aerosol Model H. Matsui https://doi.org/10.1029/2019JD032316
42. Direct Measurements of Dry and Wet Deposition of Black Carbon Over a Grassland E. Emerson et al. https://doi.org/10.1029/2018JD028954
43. Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail C. Wellmann et al. https://doi.org/10.5194/acp-20-2201-2020
44. What controls the vertical distribution of aerosol? Relationships between process sensitivity in HadGEM3–UKCA and inter-model variation from AeroCom Phase II Z. Kipling et al. https://doi.org/10.5194/acp-16-2221-2016
45. EFFICIENT CALIBRATION FOR HIGH-DIMENSIONAL COMPUTER MODEL OUTPUT USING BASIS METHODS J. Salter & D. Williamson https://doi.org/10.1615/Int.J.UncertaintyQuantification.2022039747
46. Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. https://doi.org/10.5194/acp-20-1363-2020
47. Large contribution of natural aerosols to uncertainty in indirect forcing K. Carslaw et al. https://doi.org/10.1038/nature12674
48. Modeling the Processing of Aerosol and Trace Gases in Clouds and Fogs B. Ervens https://doi.org/10.1021/cr5005887
49. Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition N. Bellouin et al. https://doi.org/10.5194/essd-12-1649-2020
50. Aerosol size distribution changes in FIREX-AQ biomass burning plumes: the impact of plume concentration on coagulation and OA condensation/evaporation N. June et al. https://doi.org/10.5194/acp-22-12803-2022
51. Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint R. Moore et al. https://doi.org/10.5194/acp-13-4235-2013
52. Effects of near-source coagulation of biomass burning aerosols on global predictions of aerosol size distributions and implications for aerosol radiative effects E. Ramnarine et al. https://doi.org/10.5194/acp-19-6561-2019
53. A new approach to modeling aerosol effects on East Asian climate: Parametric uncertainties associated with emissions, cloud microphysics, and their interactions H. Yan et al. https://doi.org/10.1002/2015JD023442
54. Impact of acidity and surface-modulated acid dissociation on cloud response to organic aerosol G. Sengupta et al. https://doi.org/10.5194/acp-24-1467-2024
55. Analysis of particulate emissions from tropical biomass burning using a global aerosol model and long-term surface observations C. Reddington et al. https://doi.org/10.5194/acp-16-11083-2016
56. A comparison of statistical emulation methodologies for multi‐wave calibration of environmental models J. Salter & D. Williamson https://doi.org/10.1002/env.2405
57. Ultrafine particles over Eastern Australia: an airborne survey W. Junkermann & J. Hacker https://doi.org/10.3402/tellusb.v67.25308
58. Performance evaluation of UKESM1 for surface ozone across the pan-tropics F. Brown et al. https://doi.org/10.5194/acp-24-12537-2024
59. Meteorological and Land Surface Properties Impacting Sea Breeze Extent and Aerosol Distribution in a Dry Environment A. Igel et al. https://doi.org/10.1002/2017JD027339
60. Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system J. Seinfeld et al. https://doi.org/10.1073/pnas.1514043113
61. Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation G. Fanourgakis et al. https://doi.org/10.5194/acp-19-8591-2019
62. Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output E. Ryan et al. https://doi.org/10.5194/gmd-11-3131-2018
63. Diagnosing aerosol–meteorological interactions on snow within Earth system models: a proof-of-concept study over High Mountain Asia C. Roychoudhury et al. https://doi.org/10.5194/esd-16-1237-2025
64. The sensitivity of Southern Ocean aerosols and cloud microphysics to sea spray and sulfate aerosol production in the HadGEM3-GA7.1 chemistry–climate model L. Revell et al. https://doi.org/10.5194/acp-19-15447-2019
65. Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing J. Johnson et al. https://doi.org/10.5194/acp-20-9491-2020
66. Constraints on global aerosol number concentration, SO2 and condensation sink in UKESM1 using ATom measurements A. Ranjithkumar et al. https://doi.org/10.5194/acp-21-4979-2021
67. A perturbed parameter ensemble of HadGEM3-GC3.05 coupled model projections: part 1: selecting the parameter combinations D. Sexton et al. https://doi.org/10.1007/s00382-021-05709-9
68. The NPF Effect on CCN Number Concentrations: A Review and Re‐Evaluation of Observations From 35 Sites Worldwide J. Ren et al. https://doi.org/10.1029/2021GL095190
69. Optical properties of carbonaceous aerosols and source impacts in the Lanzhou valley: In-situ observations by means of topographical relief S. Qi et al. https://doi.org/10.1016/j.uclim.2025.102302
70. Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? R. Johnstone et al. https://doi.org/10.1016/j.yjmcc.2015.11.018
71. The aerosol radiative effects of uncontrolled combustion of domestic waste J. Kodros et al. https://doi.org/10.5194/acp-16-6771-2016
72. Size-resolved morphological properties of the high Arctic summer aerosol during ASCOS-2008 E. Hamacher-Barth et al. https://doi.org/10.5194/acp-16-6577-2016
73. Benchmarking sampling methodology for calculations of Rayleigh light scattering properties of atmospheric molecular clusters T. Joranger et al. https://doi.org/10.1039/C9CP02573A
74. An emulator approach to stratocumulus susceptibility F. Glassmeier et al. https://doi.org/10.5194/acp-19-10191-2019
75. Aitken mode particles as CCN in aerosol- and updraft-sensitive regimes of cloud droplet formation M. Pöhlker et al. https://doi.org/10.5194/acp-21-11723-2021
76. Finding plausible and diverse variants of a climate model. Part 1: establishing the relationship between errors at weather and climate time scales D. Sexton et al. https://doi.org/10.1007/s00382-019-04625-3
77. Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators S. Coveney & R. Clayton https://doi.org/10.3389/fphys.2020.00364
78. Opinion: The importance and future development of perturbed parameter ensembles in climate and atmospheric science K. Carslaw et al. https://doi.org/10.5194/acp-26-4651-2026
79. Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes J. Vicent et al. https://doi.org/10.1109/JSTARS.2018.2875330
80. Systematic satellite observations of the impact of aerosols from passive volcanic degassing on local cloud properties S. Ebmeier et al. https://doi.org/10.5194/acp-14-10601-2014
81. An improved representation of aerosol mixing state for air quality–weather interactions R. Stevens et al. https://doi.org/10.5194/acp-22-13527-2022
82. Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity G. Mann et al. https://doi.org/10.5194/acp-14-4679-2014
83. Manifold learning for the emulation of spatial fields from computational models W. Xing et al. https://doi.org/10.1016/j.jcp.2016.07.040
84. Emulating computer models with high-dimensional count output J. Salter et al. https://doi.org/10.1098/rsta.2024.0216
85. On the relationship between aerosol model uncertainty and radiative forcing uncertainty L. Lee et al. https://doi.org/10.1073/pnas.1507050113
86. Causes and importance of new particle formation in the present‐day and preindustrial atmospheres H. Gordon et al. https://doi.org/10.1002/2017JD026844
87. Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls T. Ansari et al. https://doi.org/10.5194/acp-21-4471-2021
88. Aged boreal biomass-burning aerosol size distributions from BORTAS 2011 K. Sakamoto et al. https://doi.org/10.5194/acp-15-1633-2015
89. Major secondary aerosol formation in southern African open biomass burning plumes V. Vakkari et al. https://doi.org/10.1038/s41561-018-0170-0
90. Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide E. Ryan & O. Wild https://doi.org/10.5194/gmd-14-5373-2021
91. Oxidation Products of Biogenic Emissions Contribute to Nucleation of Atmospheric Particles F. Riccobono et al. https://doi.org/10.1126/science.1243527
92. A new chemistry option in WRF-Chem v. 3.4 for the simulation of direct and indirect aerosol effects using VBS: evaluation against IMPACT-EUCAARI data P. Tuccella et al. https://doi.org/10.5194/gmd-8-2749-2015
93. Global combustion sources of organic aerosols: model comparison with 84 AMS factor-analysis data sets A. Tsimpidi et al. https://doi.org/10.5194/acp-16-8939-2016
94. The Climatic Importance of Uncertainties in Regional Aerosol–Cloud Radiative Forcings over Recent Decades L. Regayre et al. https://doi.org/10.1175/JCLI-D-15-0127.1
95. On the causal structure between CO2 and global temperature A. Stips et al. https://doi.org/10.1038/srep21691
96. Revisiting particle dry deposition and its role in radiative effect estimates E. Emerson et al. https://doi.org/10.1073/pnas.2014761117
97. Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing L. Yuan et al. https://doi.org/10.1007/s11869-021-01041-7
98. Occurrence of pristine aerosol environments on a polluted planet D. Hamilton et al. https://doi.org/10.1073/pnas.1415440111
99. An AeroCom assessment of black carbon in Arctic snow and sea ice C. Jiao et al. https://doi.org/10.5194/acp-14-2399-2014
100. Processes Controlling the Composition and Abundance of Arctic Aerosol M. Willis et al. https://doi.org/10.1029/2018RG000602
101. Analysis and evaluation of the global aerosol optical properties simulated by an online aerosol-coupled non-hydrostatic icosahedral atmospheric model T. Dai et al. https://doi.org/10.1007/s00376-014-4098-z
102. Tropospheric ozone radiative forcing uncertainty due to pre-industrial fire and biogenic emissions M. Rowlinson et al. https://doi.org/10.5194/acp-20-10937-2020
103. Dry Deposition of Hydrophilic Black Carbon Aerosols in China X. Zhang et al. https://doi.org/10.3390/atmos14071114
104. Inferring Polluted Asian Absorbing Aerosol Properties Using Decadal Scale AERONET Measurements and a MIE Model S. Wang et al. https://doi.org/10.1029/2021GL094300
105. Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator E. Chang et al. https://doi.org/10.1371/journal.pone.0130252
106. Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach N. Bounceur et al. https://doi.org/10.5194/esd-6-205-2015
107. Evaluation of ACCMIP simulated fine-mode AOD and its implication for aerosol direct forcing H. Park et al. https://doi.org/10.1007/s13143-014-0025-6
108. A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model S. Fiddes et al. https://doi.org/10.5194/gmd-17-2641-2024
109. RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al. https://doi.org/10.3390/rs17213618
110. The importance of vertical velocity variability for estimates of the indirect aerosol effects R. West et al. https://doi.org/10.5194/acp-14-6369-2014
111. Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition J. Schmale et al. https://doi.org/10.1038/sdata.2017.3
112. Evaluating uncertainty in convective cloud microphysics using statistical emulation J. Johnson et al. https://doi.org/10.1002/2014MS000383
113. Evaluating the performance and energy efficiency of the COSMO-ART model system J. Charles et al. https://doi.org/10.1007/s00450-014-0267-7
114. Quantifying the Contribution to Uncertainty in Mortality Attributed to Household, Ambient, and Joint Exposure to PM2.5From Residential Solid Fuel Use J. Kodros et al. https://doi.org/10.1002/2017GH000115
115. A parameterization of sub-grid particle formation in sulfur-rich plumes for global- and regional-scale models R. Stevens & J. Pierce https://doi.org/10.5194/acp-13-12117-2013
116. CCN production by new particle formation in the free troposphere C. Rose et al. https://doi.org/10.5194/acp-17-1529-2017
117. Emulation and Sensitivity Analysis of the Community Multiscale Air Quality Model for a UK Ozone Pollution Episode A. Beddows et al. https://doi.org/10.1021/acs.est.6b05873
118. An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 S. Phipps et al. https://doi.org/10.5194/gmd-14-5107-2021
119. Exploring the uncertainties in the aviation soot–cirrus effect M. Righi et al. https://doi.org/10.5194/acp-21-17267-2021
120. Polar primary aerosols across the ocean-sea ice-snow-atmosphere interface: From sources to impacts J. Creamean et al. https://doi.org/10.1525/elementa.2025.00065
121. Black carbon radiative effects highly sensitive to emitted particle size when resolving mixing-state diversity H. Matsui et al. https://doi.org/10.1038/s41467-018-05635-1
122. Dry Deposition Methods Based on Turbulence Kinetic Energy: 2. Extension to Particle Deposition Using a Single‐Point Model B. Cheng et al. https://doi.org/10.1029/2022JD037803
123. Machine learning and artificial intelligence to aid climate change research and preparedness C. Huntingford et al. https://doi.org/10.1088/1748-9326/ab4e55
124. The mass and number size distributions of black carbon aerosol over Europe C. Reddington et al. https://doi.org/10.5194/acp-13-4917-2013
125. Expert elicitation of directional metocean parameters L. Astfalck et al. https://doi.org/10.1016/j.oceaneng.2018.04.047
126. Sensitivity of European blocking to physical parameters in a large ensemble climate model experiment T. Woollings et al. https://doi.org/10.1002/asl.1295
127. Advancing global aerosol simulations with size-segregated anthropogenic particle number emissions F. Xausa et al. https://doi.org/10.5194/acp-18-10039-2018
128. Characterization of size-segregated particles' turbulent flux and deposition velocity by eddy correlation method at an Arctic site A. Donateo et al. https://doi.org/10.5194/acp-23-7425-2023
129. Measuring Biosphere–Atmosphere Exchange of Short-Lived Climate Forcers and Their Precursors D. Farmer & M. Riches https://doi.org/10.1021/acs.accounts.0c00203
130. Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al. https://doi.org/10.5194/acp-22-4737-2022
131. Size distribution of vehicle emitted primary particles measured in a traffic tunnel X. Li et al. https://doi.org/10.1016/j.atmosenv.2018.07.052
132. Cloud droplet activation through oxidation of organic aerosol influenced by temperature and particle phase state J. Slade et al. https://doi.org/10.1002/2016GL072424
133. Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation L. Marshall et al. https://doi.org/10.1029/2018JD028675
134. Global sensitivity analysis of the Indian monsoon during the Pleistocene P. Araya-Melo et al. https://doi.org/10.5194/cp-11-45-2015
135. Reassessment of pre-industrial fire emissions strongly affects anthropogenic aerosol forcing D. Hamilton et al. https://doi.org/10.1038/s41467-018-05592-9
136. Significantly Enhanced Aerosol CCN Activity and Number Concentrations by Nucleation‐Initiated Haze Events: A Case Study in Urban Beijing F. Zhang et al. https://doi.org/10.1029/2019JD031457
137. Quantifying and addressing the uncertainties in tropospheric ozone and OH in a global chemistry transport model O. Wild & E. Ryan https://doi.org/10.5194/acp-26-2255-2026
138. Aerosols in the Pre-industrial Atmosphere K. Carslaw et al. https://doi.org/10.1007/s40641-017-0061-2
139. Impact on short-lived climate forcers increases projected warming due to deforestation C. Scott et al. https://doi.org/10.1038/s41467-017-02412-4
140. Boundary layer nucleation as a source of new CCN in savannah environment L. Laakso et al. https://doi.org/10.5194/acp-13-1957-2013
141. The direct and indirect radiative effects of biogenic secondary organic aerosol C. Scott et al. https://doi.org/10.5194/acp-14-447-2014
142. A global model perturbed parameter ensemble study of secondary organic aerosol formation K. Sengupta et al. https://doi.org/10.5194/acp-21-2693-2021
143. Processes controlling the annual cycle of Arctic aerosol number and size distributions B. Croft et al. https://doi.org/10.5194/acp-16-3665-2016
144. A pathway analysis of global aerosol processes N. Schutgens & P. Stier https://doi.org/10.5194/acp-14-11657-2014
145. Impacts of Aerosol Dry Deposition on Black Carbon Spatial Distributions and Radiative Effects in the Community Atmosphere Model CAM5 M. Wu et al. https://doi.org/10.1029/2017MS001219
146. Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition S. Landwehr et al. https://doi.org/10.5194/esd-12-1295-2021
147. In situ constraints on the vertical distribution of global aerosol D. Watson-Parris et al. https://doi.org/10.5194/acp-19-11765-2019
148. The importance of interstitial particle scavenging by cloud droplets in shaping the remote aerosol size distribution and global aerosol-climate effects J. Pierce et al. https://doi.org/10.5194/acp-15-6147-2015
149. Characterizing aerosols during forest fires over Uttarakhand region in India using multi-satellite remote sensing data S. Verma et al. https://doi.org/10.1016/j.asr.2022.05.051
150. Tutorial: Guide to error propagation for particle counting measurements T. Sipkens et al. https://doi.org/10.1016/j.jaerosci.2022.106091
151. Simulating the forest fire plume dispersion, chemistry, and aerosol formation using SAM-ASP version 1.0 C. Lonsdale et al. https://doi.org/10.5194/gmd-13-4579-2020
152. Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions L. Fierce et al. https://doi.org/10.1016/j.jaerosci.2024.106388
153. Improving Below‐Cloud Scavenging Coefficients of Sulfate, Nitrate, and Ammonium in PM2.5 and Implications for Numerical Simulation and Air Pollution Control L. Yao et al. https://doi.org/10.1029/2023JD039487
154. Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters N. Harvey et al. https://doi.org/10.5194/nhess-18-41-2018
155. Analysis of feedbacks between nucleation rate, survival probability and cloud condensation nuclei formation D. Westervelt et al. https://doi.org/10.5194/acp-14-5577-2014
156. Global atmospheric particle formation from CERN CLOUD measurements E. Dunne et al. https://doi.org/10.1126/science.aaf2649
157. The Impact of Changes in Cloud Water pH on Aerosol Radiative Forcing S. Turnock et al. https://doi.org/10.1029/2019GL082067
158. Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories J. Schmale et al. https://doi.org/10.5194/acp-18-2853-2018
159. Global aerosol modeling with MADE3 (v3.0) in EMAC (based on v2.53): model description and evaluation J. Kaiser et al. https://doi.org/10.5194/gmd-12-541-2019
160. Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations J. Mulcahy et al. https://doi.org/10.5194/gmd-13-6383-2020
161. Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis J. Verrelst et al. https://doi.org/10.3390/rs8080673
162. Global–regional nested simulation of particle number concentration by combing microphysical processes with an evolving organic aerosol module X. Chen et al. https://doi.org/10.5194/acp-21-9343-2021
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