57 citations as recorded by crossref.

1. An Overview of the Extratropical Storm Tracks in CMIP6 Historical Simulations M. Priestley et al. 10.1175/JCLI-D-19-0928.1
2. Are Cut-off Lows simulated better in CMIP6 compared to CMIP5? H. Pinheiro et al. 10.1007/s00382-022-06200-9
3. Challenging and Improving the Simulation of Mid‐Level Mixed‐Phase Clouds Over the High‐Latitude Southern Ocean É. Vignon et al. 10.1029/2020JD033490
4. Total ozone content, total cloud cover, and aerosol optical depth in CMIP6: simulations performance and projected changes A. Yamamoto et al. 10.1007/s00704-023-04821-6
5. Effective radiative forcing from emissions of reactive gases and aerosols – a multi-model comparison G. Thornhill et al. 10.5194/acp-21-853-2021
6. Importance of Minor‐Looking Treatments in Global Climate Models H. Kawai et al. 10.1029/2022MS003128
7. The Diurnal Temperature Range in CMIP6 Models: Climatology, Variability, and Evolution K. Wang & G. Clow 10.1175/JCLI-D-19-0897.1
8. Future changes in precipitation extremes across China based on CMIP6 models H. Xu et al. 10.1002/joc.7264
9. Recent global nonhydrostatic modeling approach without using a cumulus parameterization to understand the mechanisms underlying cloud changes due to global warming A. Noda et al. 10.1186/s40645-023-00583-x
10. Seasonal Variation of Wet Deposition of Black Carbon in Arctic Alaska T. Mori et al. 10.1029/2019JD032240
11. Marine Low Clouds and their Parameterization in Climate Models H. KAWAI & S. SHIGE 10.2151/jmsj.2020-059
12. Cloud Characteristics and Their Effects on Solar Irradiance According to the ICON Model, CLOUDNET and BSRN Observations J. Shuvalova et al. 10.3390/atmos14121769
13. Contributions of Anthropogenic Aerosol Forcing and Multidecadal Internal Variability to Mid‐20th Century Arctic Cooling—CMIP6/DAMIP Multimodel Analysis T. Aizawa et al. 10.1029/2021GL097093
14. Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China? H. Zhu et al. 10.1007/s00376-020-9289-1
15. Observational constraint on a feedback from supercooled clouds reduces projected warming uncertainty G. Cesana et al. 10.1038/s43247-024-01339-1
16. An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models J. Wei et al. 10.1007/s13131-021-1705-6
17. Modeling the impact of climate change on streamflow responses in the Kessem watershed, Middle Awash sub-basin, Ethiopia M. Assfaw et al. 10.2166/wcc.2023.541
18. Tropospheric ozone in CMIP6 simulations P. Griffiths et al. 10.5194/acp-21-4187-2021
19. Range Dynamics of Potato Cyst Nematode Globodera rostochiensis (Wollenweber, 1923) (Nematoda, Heteroderidae) under Conditions of Global Climate Change in Russia M. Pridannikov et al. 10.1134/S2075111722040099
20. Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation A. Kaps et al. 10.1109/TGRS.2023.3237008
21. Association of the cloud radiative effect with the changes in the northern edge of Hadley circulation between the CMIP5 and CMIP6 models in boreal summer S. Gui et al. 10.1007/s00704-023-04679-8
22. Evaluating modelled tropospheric columns of CH4, CO, and O3 in the Arctic using ground-based Fourier transform infrared (FTIR) measurements V. Flood et al. 10.5194/acp-24-1079-2024
23. Improved Representation of Low‐Level Mixed‐Phase Clouds in a Global Cloud‐System‐Resolving Simulation A. Noda et al. 10.1029/2021JD035223
24. On the Effect of Historical SST Patterns on Radiative Feedback T. Andrews et al. 10.1029/2022JD036675
25. The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component S. YUKIMOTO et al. 10.2151/jmsj.2019-051
26. Review of the current polar ice sheet surface mass balance and its modelling: the 2020 summer edition M. NIWANO et al. 10.5331/seppyo.83.1_27
27. Arctic black carbon during PAMARCMiP 2018 and previous aircraft experiments in spring S. Ohata et al. 10.5194/acp-21-15861-2021
28. SimCloud version 1.0: a simple diagnostic cloud scheme for idealized climate models Q. Liu et al. 10.5194/gmd-14-2801-2021
29. Changes in Dust Emissions in the Gobi Desert due to Global Warming Using MRI-ESM2.0 T. Maki et al. 10.2151/sola.2022-035
30. Evaluation of Past and Future Climate Trends under CMIP6 Scenarios for the UBNB (Abay), Ethiopia A. Alaminie et al. 10.3390/w13152110
31. The ozone–climate penalty over South America and Africa by 2100 F. Brown et al. 10.5194/acp-22-12331-2022
32. Discrepancies in Simulated Ocean Net Surface Heat Fluxes over the North Atlantic C. Liu et al. 10.1007/s00376-022-1360-7
33. Effects of Anthropogenic Forcings on Multidecadal Variability of the Sea Level Around the Japanese Coast Simulated by MRI‐ESM2.0 for CMIP6 Y. Ushijima et al. 10.1029/2022GL099987
34. RANGE DYNAMICS OF POTATO NEMATODE <i>GLOBODERA ROSTOCHIENSIS</i> (WOLLENWEBER, 1923) SKARBILOVICH, 1959 UNDER CONDITIONS OF GLOBAL CLIMATE CHANGE IN RUSSIA M. Pridannikov et al. 10.35885/1996-1499-15-3-135-159
35. Cloud Climatologies from Global Climate Models—A Comparison of CMIP5 and CMIP6 Models with Satellite Data A. Lauer et al. 10.1175/JCLI-D-22-0181.1
36. Estimated cloud-top entrainment index explains positive low-cloud-cover feedback T. Koshiro et al. 10.1073/pnas.2200635119
37. Recent progress in simulating two types of ENSO – from CMIP5 to CMIP6 M. Hou & Y. Tang 10.3389/fmars.2022.986780
38. Present and Future of Rainfall in Antarctica É. Vignon et al. 10.1029/2020GL092281
39. Current and future wind energy resources in the North Sea according to CMIP6 A. Hahmann et al. 10.5194/wes-7-2373-2022
40. Role of Interhemispheric Heat Transport and Global Atmospheric Cooling in Multidecadal Trends of Northern Hemisphere Precipitation S. Yukimoto et al. 10.1029/2022GL100335
41. Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors B. Jian et al. 10.5194/acp-21-9809-2021
42. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study C. Whaley et al. 10.5194/acp-22-5775-2022
43. The Impacts of Immersion Ice Nucleation Parameterizations on Arctic Mixed-Phase Stratiform Cloud Properties and the Arctic Radiation Budget in GEOS-5 I. Tan & D. Barahona 10.1175/JCLI-D-21-0368.1
44. Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia Z. Iqbal et al. 10.1016/j.atmosres.2021.105525
45. Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback G. Cesana et al. 10.1029/2021GL094876
46. Range Dynamics of Striped Field Mouse (Apodemus agrarius) in Northern Eurasia under Global Climate Change Based on Ensemble Species Distribution Models V. Petrosyan et al. 10.3390/biology12071034
47. Evaluation and correction analysis of the regional rainfall simulation by CMIP6 over Sudan W. Babiker et al. 10.5937/gp28-46565
48. Global and Arctic effective radiative forcing of anthropogenic gases and aerosols in MRI-ESM2.0 N. Oshima et al. 10.1186/s40645-020-00348-w
49. Improvements in Supercooled Liquid Water Simulations of Low-Level Mixed-Phase Clouds over the Southern Ocean Using a Single-Column Model T. Seiki & W. Roh 10.1175/JAS-D-19-0266.1
50. How do CMIP6 models project changes in precipitation extremes over seasons and locations across the mid hills of Nepal? R. Chhetri et al. 10.1007/s00704-021-03698-7
51. On the emergence of a predicted climate change signal: When and where it could appear over Pakistan B. Khan et al. 10.29333/ejosdr/12561
52. Marine aerosol feedback on biogeochemical cycles and the climate in the Anthropocene: lessons learned from the Pacific Ocean A. Ito et al. 10.1039/D2EA00156J
53. Assessment of the capability of CMIP6 global climate models to simulate Arctic cyclones J. Song et al. 10.1016/j.accre.2021.07.007
54. Relationship between shortwave radiation bias over the Southern Ocean and the double‐intertropical convergence zone problem in MRI‐ESM2 H. Kawai et al. 10.1002/asl.1064
55. Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model W. Kim et al. 10.5194/cp-17-2031-2021
56. Low‐Level Marine Tropical Clouds in Six CMIP6 Models Are Too Few, Too Bright but Also Too Compact and Too Homogeneous D. Konsta et al. 10.1029/2021GL097593
57. Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100) S. Mohammed et al. 10.1016/j.jhydrol.2024.130968