105 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. Mesh-Clustered Gaussian Process Emulator for Partial Differential Equation Boundary Value Problems C. Sung et al. 10.1080/00401706.2024.2320211
4. The magnitude and sources of uncertainty in global aerosol K. Carslaw et al. 10.1039/c3fd00043e
5. Uncertainty in the magnitude of aerosol‐cloud radiative forcing over recent decades L. Regayre et al. 10.1002/2014GL062029
6. 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
7. 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
8. The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei L. Lee et al. 10.5194/acp-13-8879-2013
9. New Global Sensitivity Measure Based on Fuzzy Distance C. Chen et al. 10.1061/(ASCE)EM.1943-7889.0001336
10. Uncertainty analysis of a model of wind-blown volcanic plumes M. Woodhouse et al. 10.1007/s00445-015-0959-2
11. 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
12. 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
13. Comparative Assessment of Climate Engineering Scenarios in the Presence of Parametric Uncertainty G. Tran et al. 10.1029/2019MS001787
14. Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
15. Bayesian probabilistic representation of complex systems: With application to wave load modeling S. Glavind et al. 10.1111/mice.12763
16. 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
17. Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators S. Coveney & R. Clayton 10.3389/fphys.2020.00364
18. Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator D. Watson-Parris et al. 10.5194/gmd-14-7659-2021
19. Identifying and removing structural biases in climate models with history matching D. Williamson et al. 10.1007/s00382-014-2378-z
20. 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
21. 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
22. On the relationship between aerosol model uncertainty and radiative forcing uncertainty L. Lee et al. 10.1073/pnas.1507050113
23. Parallel partial Gaussian process emulation for computer models with massive output M. Gu & J. Berger 10.1214/16-AOAS934
24. Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
25. 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
26. Global sensitivity analysis of the Indian monsoon during the Pleistocene P. Araya-Melo et al. 10.5194/cp-11-45-2015
27. An application of the Kriging method in global sensitivity analysis with parameter uncertainty P. Wang et al. 10.1016/j.apm.2013.01.019
28. An emulator approach to stratocumulus susceptibility F. Glassmeier et al. 10.5194/acp-19-10191-2019
29. 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
30. 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
31. Global air quality and climate A. Fiore et al. 10.1039/c2cs35095e
32. 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
33. 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
34. 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
35. Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
36. Bounding Global Aerosol Radiative Forcing of Climate Change N. Bellouin et al. 10.1029/2019RG000660
37. 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
38. An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6 T. Eidhammer et al. 10.5194/gmd-17-7835-2024
39. The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design C. Timmreck et al. 10.5194/gmd-11-2581-2018
40. 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
41. Reviewing Climate Changes Modeling Methods V. Pepelyaev et al. 10.1007/s10559-023-00574-2
42. Meta-modeling of a simulation chain for urban air quality J. Hammond et al. 10.1186/s40323-020-00173-2
43. Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity C. Fletcher et al. 10.5194/acp-18-17529-2018
44. Mitigation of PM<sub>2.5</sub> and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period Y. Chen et al. 10.5194/acp-20-499-2020
45. 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
46. Atmospheric-methane source and sink sensitivity analysis using Gaussian process emulation A. Stell et al. 10.5194/acp-21-1717-2021
47. A global model perturbed parameter ensemble study of secondary organic aerosol formation K. Sengupta et al. 10.5194/acp-21-2693-2021
48. Complex model calibration through emulation, a worked example for a stochastic epidemic model M. Dunne et al. 10.1016/j.epidem.2022.100574
49. 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
50. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
51. Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. 10.5194/acp-20-1363-2020
52. Exploratory ensemble designs for environmental models using k‐extended Latin Hypercubes D. Williamson 10.1002/env.2335
53. Multiobjective constraints for climate model parameter choices: PragmaticPareto fronts in CESM1 B. Langenbrunner & J. Neelin 10.1002/2017MS000942
54. 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
55. 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
56. Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al. 10.5194/acp-22-4737-2022
57. 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
58. 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
59. Toward Efficient Calibration of Higher‐Resolution Earth System Models C. Fletcher et al. 10.1029/2021MS002836
60. 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
61. 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
62. Emulation of a process-based estuarine hydrodynamic model L. Chen et al. 10.1080/02626667.2018.1447112
63. 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
64. An overview of methods to evaluate uncertainty of deterministic models in decision support L. Uusitalo et al. 10.1016/j.envsoft.2014.09.017
65. 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
66. Online coupled regional meteorology chemistry models in Europe: current status and prospects A. Baklanov et al. 10.5194/acp-14-317-2014
67. Boundary layer nucleation as a source of new CCN in savannah environment L. Laakso et al. 10.5194/acp-13-1957-2013
68. 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
69. Emulation and Sobol' sensitivity analysis of an atmospheric dispersion model applied to the Fukushima nuclear accident S. Girard et al. 10.1002/2015JD023993
70. Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics U. Proske et al. 10.1029/2022MS003571
71. 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
72. 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
73. 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
74. Demystifying the complex nature of stratocumulus clouds with machine learning R. Sansom 10.1002/wea.4001
75. Evaluating uncertainty in convective cloud microphysics using statistical emulation J. Johnson et al. 10.1002/2014MS000383
76. 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
77. Unknown Eruption Source Parameters Cause Large Uncertainty in Historical Volcanic Radiative Forcing Reconstructions L. Marshall et al. 10.1029/2020JD033578
78. Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection 10.2903/j.efsa.2016.4578
79. Analysis of present day and future OH and methane lifetime in the ACCMIP simulations A. Voulgarakis et al. 10.5194/acp-13-2563-2013
80. Aerosols in the Pre-industrial Atmosphere K. Carslaw et al. 10.1007/s40641-017-0061-2
81. Meta-modeling of ADMS-Urban by dimension reduction and emulation V. Mallet et al. 10.1016/j.atmosenv.2018.04.009
82. Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation L. Marshall et al. 10.1029/2018JD028675
83. Meteorological and Land Surface Properties Impacting Sea Breeze Extent and Aerosol Distribution in a Dry Environment A. Igel et al. 10.1002/2017JD027339
84. Environmental Controls on Tropical Sea Breeze Convection and Resulting Aerosol Redistribution J. Park et al. 10.1029/2019JD031699
85. Using Emulators to Understand the Sensitivity of Deep Convective Clouds and Hail to Environmental Conditions C. Wellmann et al. 10.1029/2018MS001465
86. Emulator-based global sensitivity analysis for flow-like landslide run-out models H. Zhao et al. 10.1007/s10346-021-01690-w
87. 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
88. A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models M. Gunarathna et al. 10.3390/agronomy10020285
89. 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
90. Overview of integrated models for bioprocess engineering A. Velayudhan 10.1016/j.coche.2014.09.007
91. Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters V. Karydis et al. 10.5194/acp-12-9041-2012
92. 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
93. 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
94. 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
95. Mapping the uncertainty in global CCN using emulation L. Lee et al. 10.5194/acp-12-9739-2012
96. Atmospheric inversion of SO<sub>2</sub> and primary aerosol emissions for the year 2010 N. Huneeus et al. 10.5194/acp-13-6555-2013
97. Emulating dynamic non-linear simulators using Gaussian processes H. Mohammadi et al. 10.1016/j.csda.2019.05.006
98. Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. 10.1029/2021GH000391
99. 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
100. 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
101. Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones F. He et al. 10.1175/MWR-D-17-0265.1
102. 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
103. 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
104. 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
105. Primary versus secondary contributions to particle number concentrations in the European boundary layer C. Reddington et al. 10.5194/acp-11-12007-2011