64 citations as recorded by crossref.

1. 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
2. Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. 10.5194/acp-20-1363-2020
3. Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output E. Ryan et al. 10.5194/gmd-11-3131-2018
4. 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
5. Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling J. Salter et al. 10.5194/acp-24-6251-2024
6. Sources of uncertainty in atmospheric dispersion modeling in support of Comprehensive Nuclear–Test–Ban Treaty monitoring and verification system S. Mekhaimr & M. Abdel Wahab 10.1016/j.apr.2019.03.008
7. Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
8. 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. 10.1029/2023JD039487
9. Impact of future Arctic shipping on high‐latitude black carbon deposition J. Browse et al. 10.1002/grl.50876
10. Emulator-based global sensitivity analysis for flow-like landslide run-out models H. Zhao et al. 10.1007/s10346-021-01690-w
11. Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. 10.1029/2021GH000391
12. Sensitivity experiments on the response of Vb cyclones to sea surface temperature and soil moisture changes M. Messmer et al. 10.5194/esd-8-477-2017
13. Emulation and Sensitivity Analysis of the Community Multiscale Air Quality Model for a UK Ozone Pollution Episode A. Beddows et al. 10.1021/acs.est.6b05873
14. Study of new particle formation events in southern Italy A. Dinoi et al. 10.1016/j.atmosenv.2020.117920
15. EFFICIENT CALIBRATION FOR HIGH-DIMENSIONAL COMPUTER MODEL OUTPUT USING BASIS METHODS J. Salter & D. Williamson 10.1615/Int.J.UncertaintyQuantification.2022039747
16. Parallel partial Gaussian process emulation for computer models with massive output M. Gu & J. Berger 10.1214/16-AOAS934
17. Uncertainty in the magnitude of aerosol‐cloud radiative forcing over recent decades L. Regayre et al. 10.1002/2014GL062029
18. The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei L. Lee et al. 10.5194/acp-13-8879-2013
19. 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
20. 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. 10.1002/2015JD023442
21. Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models X. Huo et al. 10.1029/2018MS001573
22. Occurrence of pristine aerosol environments on a polluted planet D. Hamilton et al. 10.1073/pnas.1415440111
23. Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) P. Young et al. 10.5194/acp-13-2063-2013
24. Optimization of the Eddy‐Diffusivity/Mass‐Flux Shallow Cumulus and Boundary‐Layer Parameterization Using Surrogate Models W. Langhans et al. 10.1029/2018MS001449
25. 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
26. Comparative Assessment of Climate Engineering Scenarios in the Presence of Parametric Uncertainty G. Tran et al. 10.1029/2019MS001787
27. Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models H. Wan et al. 10.5194/gmd-7-1961-2014
28. The direct and indirect radiative effects of biogenic secondary organic aerosol C. Scott et al. 10.5194/acp-14-447-2014
29. Boundary layer nucleation as a source of new CCN in savannah environment L. Laakso et al. 10.5194/acp-13-1957-2013
30. Influences of aerosol physiochemical properties and new particle formation on CCN activity from observation at a suburban site of China Y. Li et al. 10.1016/j.atmosres.2017.01.009
31. Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
32. The magnitude and sources of uncertainty in global aerosol K. Carslaw et al. 10.1039/c3fd00043e
33. Building a traceable climate model hierarchy with multi-level emulators G. Tran et al. 10.5194/ascmo-2-17-2016
34. Mesh-Clustered Gaussian Process Emulator for Partial Differential Equation Boundary Value Problems C. Sung et al. 10.1080/00401706.2024.2320211
35. 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
36. Black carbon simulations using a size‐ and mixing‐state‐resolved three‐dimensional model: 1. Radiative effects and their uncertainties H. Matsui 10.1002/2015JD023998
37. Calibration and Uncertainty Quantification of a Gravity Wave Parameterization: A Case Study of the Quasi‐Biennial Oscillation in an Intermediate Complexity Climate Model L. Mansfield & A. Sheshadri 10.1029/2022MS003245
38. 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
39. Iodine observed in new particle formation events in the Arctic atmosphere during ACCACIA J. Allan et al. 10.5194/acp-15-5599-2015
40. Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al. 10.5194/acp-22-4737-2022
41. A pathway analysis of global aerosol processes N. Schutgens & P. Stier 10.5194/acp-14-11657-2014
42. Rain‐aerosol relationships influenced by wind speed Y. Yang et al. 10.1002/2016GL067770
43. A comparison of two chemistry and aerosol schemes on the regional scale and the resulting impact on radiative properties and liquid- and ice-phase aerosol–cloud interactions F. Glassmeier et al. 10.5194/acp-17-8651-2017
44. Dry Deposition of Atmospheric Aerosols: Approaches, Observations, and Mechanisms D. Farmer et al. 10.1146/annurev-physchem-090519-034936
45. 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
46. 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
47. Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing L. Regayre et al. 10.5194/acp-23-8749-2023
48. Mechanistic site-based emulation of a global ocean biogeochemical model (MEDUSA 1.0) for parametric analysis and calibration: an application of the Marine Model Optimization Testbed (MarMOT 1.1) J. Hemmings et al. 10.5194/gmd-8-697-2015
49. Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
50. Multiobjective constraints for climate model parameter choices: PragmaticPareto fronts in CESM1 B. Langenbrunner & J. Neelin 10.1002/2017MS000942
51. 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
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. Quantifying the causes of differences in tropospheric OH within global models J. Nicely et al. 10.1002/2016JD026239
54. The complex response of Arctic aerosol to sea-ice retreat J. Browse et al. 10.5194/acp-14-7543-2014
55. 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
56. 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
57. Quantifying sensitivities of ice crystal number and sources of ice crystal number variability in CAM 5.1 using the adjoint of a physically based cirrus formation parameterization B. Sheyko et al. 10.1002/2014JD022457
58. Large contribution of natural aerosols to uncertainty in indirect forcing K. Carslaw et al. 10.1038/nature12674
59. Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity C. Fletcher et al. 10.5194/acp-18-17529-2018
60. Multi-level emulation of complex climate model responses to boundary forcing data G. Tran et al. 10.1007/s00382-018-4205-4
61. Atmospheric-methane source and sink sensitivity analysis using Gaussian process emulation A. Stell et al. 10.5194/acp-21-1717-2021
62. 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
63. 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. 10.5194/acp-13-10969-2013
64. Uncertainty quantification for evaluating the impacts of fracture zone on pressure build‐up and ground surface uplift during geological CO2 sequestration J. Bao et al. 10.1002/ghg.1456