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GFDL Global Climate Models


GFDL’s CM4 consists of AM4 atmosphere at approximately 1o resolution with 33 levels and sufficient chemistry to simulate aerosols (including aerosol indirect effect) from precursor emissions; OM4 MOM6-based ocean at 1/4o resolution with 75 levels using hybrid pressure/isopycnal vertical coordinate; SIS2 sea ice with radiative transfer and C-grid dynamics for compatibility with MOM6; and LM4 land model with dynamic vegetation.


SPEAR models were developed as a next generation GFDL modeling system for seasonal to multidecadal prediction and projection. The SPEAR models share many components with the recently developed GFDL CM4 model but with configuration and physical parameterization choices in SPEAR geared toward physical climate prediction and projection on seasonal to decadal time scales.


GFDL’s successful model, CM2.1, was used as the starting point to develop the next-generation CM3 coupled model. The major development effort for CM3 focused on the atmosphere component. The update from the CM2.1 atmospheric component to the new AM3 component in CM3 (Donner et al., 2011) was intended to allow us to address important questions related to aerosol-cloud interactions, including indirect effects of aerosols, chemistry-climate interactions, and stratospheric chemistry


The CM2.5 model is a descendant of the GFDL CM2.1 model that incorporates higher spatial resolution and a significantly improved land model (LM3). As a result of these enhancements, the CM2.5 model has a significantly improved simulation of many aspects of climate, particularly hydroclimate over continental regions and aspects of ocean circulation.


The GFDL Forecast-oriented Low Ocean Resolution version of CM2.5 (FLOR) model is a descendent of the CM2.5 model and CM2.1 model. The FLOR model incorporates the higher horizontal resolution in the atmosphere and land, higher vertical resolution in the atmosphere, and significantly improved land model (LM3) from CM2.5. The FLOR model also uses the relatively low-resolution ocean and sea ice components of CM2.1. These choices create a coupled model that is relatively computationally efficient, but can be used to address problems of regional climate and extremes.