Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations

By E. Volodin, N. A. Dianskii, A. Gusev. 2010

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Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions (2014, background): “This is most evident in INMCM4, but also in GFDL-CM3, CNRM-CM5 and IPSL-CM5A which all show a distinct but small representation of the cloudy state over land.”
Machine learning methods for crop yield prediction and climate change impact assessment in agriculture (2018, positive): “Individual models used in this analysis include MRICGCM3 (Yukimoto et al 2012), GFDL-ESM2M (Dunne et al 2012), INMCM4 (Volodin et al 2010), CNRM-CM5 (Voldoire et al 2013), BNU-ESM (Ji et al 2014), IPSL-CM5A-LR and IPSL-CM5A-MR (Dufresne et al 2013), BCC-CSM1-1-m (Xin et al 2012), CanESM2 (Chylek et ”
A multi-model comparison of Atlantic multidecadal variability (2014, neutral): “…et al. 2013), the Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC) (Bellucci et al. 2008), the Model for Interdisciplinary Research on Climate (MIROC) (K-1 model developers 2004; Oka et al. 2006) and the Institute of Numerical Mathematics Climate Model (INM-CM4) (Volodin et al. 2010).”