109 citations as recorded by crossref.

1. A machine-learning-based global sea-surface iodide distribution T. Sherwen et al. 10.5194/essd-11-1239-2019
2. Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition N. Bellouin et al. 10.5194/essd-12-1649-2020
3. Habitability in 4D: Predicting the Climates of Earth Analogs across Rotation and Orbital Configurations A. Adams et al. 10.3847/1538-4357/ada3c8
4. Mesh-Clustered Gaussian Process Emulator for Partial Differential Equation Boundary Value Problems C. Sung et al. 10.1080/00401706.2024.2320211
5. The magnitude and sources of uncertainty in global aerosol K. Carslaw et al. 10.1039/c3fd00043e
6. Uncertainty in the magnitude of aerosol‐cloud radiative forcing over recent decades L. Regayre et al. 10.1002/2014GL062029
7. Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model K. Aleksankina et al. 10.5194/acp-19-2881-2019
8. Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model R. Morales Betancourt & A. Nenes 10.5194/acp-14-4809-2014
9. The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei L. Lee et al. 10.5194/acp-13-8879-2013
10. New Global Sensitivity Measure Based on Fuzzy Distance C. Chen et al. 10.1061/(ASCE)EM.1943-7889.0001336
11. Uncertainty analysis of a model of wind-blown volcanic plumes M. Woodhouse et al. 10.1007/s00445-015-0959-2
12. Processes controlling the vertical aerosol distribution in marine stratocumulus regions – a sensitivity study using the climate model NorESM1-M L. Frey et al. 10.5194/acp-21-577-2021
13. Development of a Regional Chemical Transport Model with Size-Resolved Aerosol Microphysics and Its Application on Aerosol Number Concentration Simulation over China X. Chen et al. 10.2151/sola.2014-017
14. Comparative Assessment of Climate Engineering Scenarios in the Presence of Parametric Uncertainty G. Tran et al. 10.1029/2019MS001787
15. Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
16. Bayesian probabilistic representation of complex systems: With application to wave load modeling S. Glavind et al. 10.1111/mice.12763
17. 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. 10.1007/s00382-019-04625-3
18. Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators S. Coveney & R. Clayton 10.3389/fphys.2020.00364
19. Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator D. Watson-Parris et al. 10.5194/gmd-14-7659-2021
20. Identifying and removing structural biases in climate models with history matching D. Williamson et al. 10.1007/s00382-014-2378-z
21. Model emulation to understand the joint effects of ice-nucleating particles and secondary ice production on deep convective anvil cirrus R. Hawker et al. 10.5194/acp-21-17315-2021
22. Emulation of a Chemical Transport Model to Assess Air Quality under Future Emission Scenarios for the Southwest of Western Australia S. Vander Hoorn et al. 10.3390/atmos13122009
23. On the relationship between aerosol model uncertainty and radiative forcing uncertainty L. Lee et al. 10.1073/pnas.1507050113
24. Parallel partial Gaussian process emulation for computer models with massive output M. Gu & J. Berger 10.1214/16-AOAS934
25. Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
26. Impact of the modal aerosol scheme GLOMAP-mode on aerosol forcing in the Hadley Centre Global Environmental Model N. Bellouin et al. 10.5194/acp-13-3027-2013
27. Global sensitivity analysis of the Indian monsoon during the Pleistocene P. Araya-Melo et al. 10.5194/cp-11-45-2015
28. An application of the Kriging method in global sensitivity analysis with parameter uncertainty P. Wang et al. 10.1016/j.apm.2013.01.019
29. An emulator approach to stratocumulus susceptibility F. Glassmeier et al. 10.5194/acp-19-10191-2019
30. Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model L. Revell et al. 10.5194/acp-18-16155-2018
31. Finding plausible and diverse variants of a climate model. Part II: development and validation of methodology A. Karmalkar et al. 10.1007/s00382-019-04617-3
32. Global air quality and climate A. Fiore et al. 10.1039/c2cs35095e
33. Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters N. Harvey et al. 10.5194/nhess-18-41-2018
34. Weakening of tropical sea breeze convective systems through interactions of aerosol, radiation, and soil moisture J. Park & S. van den Heever 10.5194/acp-22-10527-2022
35. On Approaches to Analyze the Sensitivity of Simulated Hydrologic Fluxes to Model Parameters in the Community Land Model J. Bao et al. 10.3390/w7126662
36. Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
37. Bounding Global Aerosol Radiative Forcing of Climate Change N. Bellouin et al. 10.1029/2019RG000660
38. Estimation of Diffusional Growth Rate and Reassessing Existing Parameterizations for Monsoon Precipitating Clouds: A Process-Based Approach M. Bhowmik et al. 10.1007/s00024-025-03686-2
39. A perturbed parameter ensemble of HadGEM3-GC3.05 coupled model projections: part 1: selecting the parameter combinations D. Sexton et al. 10.1007/s00382-021-05709-9
40. Sensitivities of warm conveyor belt ascent, associated precipitation characteristics and large‐scale flow pattern: Insights from a perturbed parameter ensemble A. Oertel et al. 10.1002/qj.4986
41. An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6 T. Eidhammer et al. 10.5194/gmd-17-7835-2024
42. The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design C. Timmreck et al. 10.5194/gmd-11-2581-2018
43. Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study K. Aleksankina et al. 10.5194/gmd-11-1653-2018
44. Reviewing Climate Changes Modeling Methods V. Pepelyaev et al. 10.1007/s10559-023-00574-2
45. Meta-modeling of a simulation chain for urban air quality J. Hammond et al. 10.1186/s40323-020-00173-2
46. Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity C. Fletcher et al. 10.5194/acp-18-17529-2018
47. Mitigation of PM2.5 and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period Y. Chen et al. 10.5194/acp-20-499-2020
48. Sensitivity Analysis of Plant- and Cultivar-Specific Parameters of APSIM-Sugar Model: Variation between Climates and Management Conditions M. Gunarathna et al. 10.3390/agronomy9050242
49. Atmospheric-methane source and sink sensitivity analysis using Gaussian process emulation A. Stell et al. 10.5194/acp-21-1717-2021
50. A global model perturbed parameter ensemble study of secondary organic aerosol formation K. Sengupta et al. 10.5194/acp-21-2693-2021
51. Complex model calibration through emulation, a worked example for a stochastic epidemic model M. Dunne et al. 10.1016/j.epidem.2022.100574
52. Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6) R. Pathak et al. 10.3389/fclim.2021.670740
53. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
54. Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. 10.5194/acp-20-1363-2020
55. Exploratory ensemble designs for environmental models using k‐extended Latin Hypercubes D. Williamson 10.1002/env.2335
56. Multiobjective constraints for climate model parameter choices: PragmaticPareto fronts in CESM1 B. Langenbrunner & J. Neelin 10.1002/2017MS000942
57. Uncertainties in anthropogenic aerosol concentrations and direct radiative forcing induced by emission inventories in eastern China W. Chang et al. 10.1016/j.atmosres.2015.06.021
58. A novel approach for determining source–receptor relationships in model simulations: a case study of black carbon transport in northern hemisphere winter P. Ma et al. 10.1088/1748-9326/8/2/024042
59. Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al. 10.5194/acp-22-4737-2022
60. Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models M. Fischer et al. 10.5194/wcd-5-511-2024
61. Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach N. Bounceur et al. 10.5194/esd-6-205-2015
62. Toward Efficient Calibration of Higher‐Resolution Earth System Models C. Fletcher et al. 10.1029/2021MS002836
63. Modelling of primary aerosols in the chemical transport model MOCAGE: development and evaluation of aerosol physical parameterizations B. Sič et al. 10.5194/gmd-8-381-2015
64. Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing J. Johnson et al. 10.5194/acp-20-9491-2020
65. Emulation of a process-based estuarine hydrodynamic model L. Chen et al. 10.1080/02626667.2018.1447112
66. The Climatic Importance of Uncertainties in Regional Aerosol–Cloud Radiative Forcings over Recent Decades L. Regayre et al. 10.1175/JCLI-D-15-0127.1
67. An overview of methods to evaluate uncertainty of deterministic models in decision support L. Uusitalo et al. 10.1016/j.envsoft.2014.09.017
68. Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth B. Sič et al. 10.5194/amt-9-5535-2016
69. Online coupled regional meteorology chemistry models in Europe: current status and prospects A. Baklanov et al. 10.5194/acp-14-317-2014
70. Boundary layer nucleation as a source of new CCN in savannah environment L. Laakso et al. 10.5194/acp-13-1957-2013
71. Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. 10.1016/j.isprsjprs.2021.11.016
72. Emulation and Sobol' sensitivity analysis of an atmospheric dispersion model applied to the Fukushima nuclear accident S. Girard et al. 10.1002/2015JD023993
73. Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics U. Proske et al. 10.1029/2022MS003571
74. The evolution of biomass-burning aerosol size distributions due to coagulation: dependence on fire and meteorological details and parameterization K. Sakamoto et al. 10.5194/acp-16-7709-2016
75. Uncertainties in the simulated intercontinental transport of air pollutants in the springtime from emission and meteorological inputs Q. Ye et al. 10.1016/j.atmosenv.2022.119431
76. Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls T. Ansari et al. 10.5194/acp-21-4471-2021
77. Demystifying the complex nature of stratocumulus clouds with machine learning R. Sansom 10.1002/wea.4001
78. Evaluating uncertainty in convective cloud microphysics using statistical emulation J. Johnson et al. 10.1002/2014MS000383
79. The empirical correlations between PM2.5, PM10 and AOD in the Beijing metropolitan region and the PM2.5, PM10 distributions retrieved by MODIS L. Kong et al. 10.1016/j.envpol.2016.05.085
80. Unknown Eruption Source Parameters Cause Large Uncertainty in Historical Volcanic Radiative Forcing Reconstructions L. Marshall et al. 10.1029/2020JD033578
81. Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection 10.2903/j.efsa.2016.4578
82. Analysis of present day and future OH and methane lifetime in the ACCMIP simulations A. Voulgarakis et al. 10.5194/acp-13-2563-2013
83. Aerosols in the Pre-industrial Atmosphere K. Carslaw et al. 10.1007/s40641-017-0061-2
84. Meta-modeling of ADMS-Urban by dimension reduction and emulation V. Mallet et al. 10.1016/j.atmosenv.2018.04.009
85. Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation L. Marshall et al. 10.1029/2018JD028675
86. Meteorological and Land Surface Properties Impacting Sea Breeze Extent and Aerosol Distribution in a Dry Environment A. Igel et al. 10.1002/2017JD027339
87. Environmental Controls on Tropical Sea Breeze Convection and Resulting Aerosol Redistribution J. Park et al. 10.1029/2019JD031699
88. Using Emulators to Understand the Sensitivity of Deep Convective Clouds and Hail to Environmental Conditions C. Wellmann et al. 10.1029/2018MS001465
89. Emulator-based global sensitivity analysis for flow-like landslide run-out models H. Zhao et al. 10.1007/s10346-021-01690-w
90. Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity G. Mann et al. 10.5194/acp-14-4679-2014
91. A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models M. Gunarathna et al. 10.3390/agronomy10020285
92. Simulating organic aerosol in Delhi with WRF-Chem using the volatility-basis-set approach: exploring model uncertainty with a Gaussian process emulator E. Reyes-Villegas et al. 10.5194/acp-23-5763-2023
93. Overview of integrated models for bioprocess engineering A. Velayudhan 10.1016/j.coche.2014.09.007
94. Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters V. Karydis et al. 10.5194/acp-12-9041-2012
95. 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. 10.5194/acp-16-2221-2016
96. Constraining aerosol–cloud adjustments by uniting surface observations with a perturbed parameter ensemble A. Mikkelsen et al. 10.5194/acp-25-4547-2025
97. Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and Their Radiative Forcing M. Yoshioka et al. 10.1029/2019MS001628
98. Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity O. Wild et al. 10.5194/acp-20-4047-2020
99. Mapping the uncertainty in global CCN using emulation L. Lee et al. 10.5194/acp-12-9739-2012
100. Atmospheric inversion of SO2 and primary aerosol emissions for the year 2010 N. Huneeus et al. 10.5194/acp-13-6555-2013
101. Emulating dynamic non-linear simulators using Gaussian processes H. Mohammadi et al. 10.1016/j.csda.2019.05.006
102. Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. 10.1029/2021GH000391
103. Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model J. Wang et al. 10.5194/gmd-12-4261-2019
104. The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing J. Johnson et al. 10.5194/acp-18-13031-2018
105. Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones F. He et al. 10.1175/MWR-D-17-0265.1
106. Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF L. Regayre et al. 10.5194/acp-18-9975-2018
107. Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint R. Moore et al. 10.5194/acp-13-4235-2013
108. Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail C. Wellmann et al. 10.5194/acp-20-2201-2020
109. Primary versus secondary contributions to particle number concentrations in the European boundary layer C. Reddington et al. 10.5194/acp-11-12007-2011