46 citations as recorded by crossref.

1. Assessing Parameter Importance of the Weather Research and Forecasting Model Based On Global Sensitivity Analysis Methods D. Ji et al. https://doi.org/10.1002/2017JD027348
2. Evaluating Marine Stratocumulus Clouds in the CNRM‐CM6‐1 Model Using Short‐Term Hindcasts F. Brient et al. https://doi.org/10.1029/2018MS001461
3. Distribution modelling and multi-scale landscape connectivity highlight important areas for the conservation of savannah elephants D. Zacarias & R. Loyola https://doi.org/10.1016/j.biocon.2018.05.014
4. An Objective and Efficient Method for Assessing the Impact of Reduced‐Precision Calculations On Solution Correctness S. Zhang et al. https://doi.org/10.1029/2019MS001817
5. Numerically Relevant Timescales in the MG2 Microphysics Model S. Santos et al. https://doi.org/10.1029/2019MS001972
6. Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model Y. Qian et al. https://doi.org/10.1007/s00382-023-06977-3
7. Uncertainties in simulated El Niño–Southern Oscillation arising from internal climate variability C. Sun et al. https://doi.org/10.1002/asl.805
8. Intercomparison of two model climates simulated by a unified weather-climate model system (GRIST), part I: mean state Z. Fu et al. https://doi.org/10.1007/s00382-024-07205-2
9. Arctic budget study of intermember variability using HIRHAM5 ensemble simulations A. Sommerfeld et al. https://doi.org/10.1002/2015JD023153
10. Sowing Storms: How Model Timestep Can Control Tropical Cyclone Frequency in a GCM C. Zarzycki https://doi.org/10.1029/2021MS002791
11. The very-high-resolution configuration of the EC-Earth global model for HighResMIP E. Moreno-Chamarro et al. https://doi.org/10.5194/gmd-18-461-2025
12. A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales H. Ma et al. https://doi.org/10.5194/gmd-14-73-2021
13. Climate model forecast biases assessed with a perturbed physics ensemble D. Mulholland et al. https://doi.org/10.1007/s00382-016-3407-x
14. Early Development and Tuning of a Global Coupled Cloud Resolving Model, and its Fast Response to Increasing CO2 T. Mauritsen et al. https://doi.org/10.16993/tellusa.54
15. Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models M. Fischer et al. https://doi.org/10.5194/wcd-5-511-2024
16. Comparing CAM5 and Superparameterized CAM5 Simulations of Summer Precipitation Characteristics over Continental East Asia: Mean State, Frequency–Intensity Relationship, Diurnal Cycle, and Influencing Factors Y. Zhang & H. Chen https://doi.org/10.1175/JCLI-D-15-0342.1
17. Quantitative Sensitivity Analysis of Physical Parameterizations for Cases of Deep Convection in the NASA GEOS-5 D. Posselt et al. https://doi.org/10.1175/JCLI-D-15-0250.1
18. PAGCM: A scalable parallel spectral‐based atmospheric general circulation model X. Ren et al. https://doi.org/10.1002/cpe.5290
19. Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling X. Li et al. https://doi.org/10.5194/gmd-16-2975-2023
20. Machine Learning Predictions of a Multiresolution Climate Model Ensemble G. Anderson & D. Lucas https://doi.org/10.1029/2018GL077049
21. Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method T. Zhang et al. https://doi.org/10.5194/gmd-11-5189-2018
22. Investigation of short-term effective radiative forcing of fire aerosols over North America using nudged hindcast ensembles Y. Liu et al. https://doi.org/10.5194/acp-18-31-2018
23. Impact of Nudging Strategy on the Climate Representativeness and Hindcast Skill of Constrained EAMv1 Simulations J. Sun et al. https://doi.org/10.1029/2019MS001831
24. Representation by two climate models of the dynamical and diabatic processes involved in the development of an explosively deepening cyclone during NAWDEX D. Flack et al. https://doi.org/10.5194/wcd-2-233-2021
25. Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations Y. Qian et al. https://doi.org/10.1029/2018JD028927
26. Resolution dependence of precipitation statistical fidelity in hindcast simulations T. O'Brien et al. https://doi.org/10.1002/2016MS000671
27. Forced advection problems for testing transport schemes M. Zerroukat & T. Allen https://doi.org/10.1002/qj.3721
28. Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1 P. Ma et al. https://doi.org/10.5194/gmd-15-2881-2022
29. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model B. Yang et al. https://doi.org/10.1007/s10546-016-0185-2
30. Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1 H. Wan et al. https://doi.org/10.5194/gmd-14-1921-2021
31. Cloud Process Coupling and Time Integration in the E3SM Atmosphere Model S. Santos et al. https://doi.org/10.1029/2020MS002359
32. Investigation of the effect of the time step on the physics–dynamics interaction in CAM5 using an idealized tropical cyclone experiment X. Li et al. https://doi.org/10.1007/s00382-020-05284-5
33. Short‐term time step convergence in a climate model H. Wan et al. https://doi.org/10.1002/2014MS000368
34. Fast and Slow Responses of Atmospheric Energy Budgets to Perturbed Cloud and Convection Processes in an Atmospheric Global Climate Model B. Yang et al. https://doi.org/10.1029/2023GL104305
35. Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations T. Schneider et al. https://doi.org/10.1002/2017GL076101
36. A scalable parallel algorithm for atmospheric general circulation models on a multi-core cluster Y. Wang et al. https://doi.org/10.1016/j.future.2017.02.008
37. Can nudging be used to quantify model sensitivities in precipitation and cloud forcing? G. Lin et al. https://doi.org/10.1002/2016MS000659
38. A new and inexpensive non-bit-for-bit solution reproducibility test based on time step convergence (TSC1.0) H. Wan et al. https://doi.org/10.5194/gmd-10-537-2017
39. 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
40. An Overview of the Atmospheric Component of the Energy Exascale Earth System Model P. Rasch et al. https://doi.org/10.1029/2019MS001629
41. Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale S. Mahajan et al. https://doi.org/10.1016/j.procs.2017.05.259
42. The Art and Science of Climate Model Tuning F. Hourdin et al. https://doi.org/10.1175/BAMS-D-15-00135.1
43. Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects S. Zhang et al. https://doi.org/10.5194/gmd-15-6787-2022
44. 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 https://doi.org/10.1029/2022MS003245
45. The climate effects of increasing ocean albedo: an idealized representation of solar geoengineering B. Kravitz et al. https://doi.org/10.5194/acp-18-13097-2018
46. Estudios de conectividad del paisaje en América Latina: retos de investigación E. Leija & M. Mendoza https://doi.org/10.21829/myb.2021.2712032