Abstract
Model calibration (or "tuning") is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major U.S. climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present day radiative imbalance vs. the implied balance in the pre-industrial as a target.
Highlights
Simulation has become an essential tool for understanding processes in the Earth system, interpreting observations and for making predictions over short, medium, and long terms
To avoid dealing with the lack of sufficient observational data from the 19th century, some modeling groups alternatively choose to tune to present-day (PD) conditions, including an energy imbalance at the top of the atmosphere (TOA) as inferred from ocean observations today (Loeb et al, 2009). Another tuning target is sometimes referred to as the radiative forcing perturbation (RFP), or the effective radiative forcing, and is the change in net flux which occurs in a multiyear integration with specified climatological sea surface temperatures (SSTs) when emissions, long-lived greenhouse gas (GHG) concentrations, and solar irradiance are changed from preindustrial to present-day values
The radiative imbalance can be affected in two ways: by adjusting internal parameters and/or by using a different historical forcing. Four models adjust their historical aerosol forcing: GISS, though only in its noninteractive runs, aims for an indirect aerosol forcing of −1 W m−2 (Schmidt et al, 2014); NCAR CESM and Department of Energy (DOE) Accelerated Climate Modeling for Energy (ACME) tune for a substantive positive effective radiative forcing at near-present conditions; GFDL AM3 constrained its ratio of Cess sensitivity to RFP to be close to its value in its prior-generation coupled model, which implied an aerosol forcing around −1.6 W m−2 in AM3
Summary
Simulation has become an essential tool for understanding processes in the Earth system, interpreting observations and for making predictions over short (weather), medium (seasonal), and long (climate) terms. The complexity of this system is evident in the myriad processes involved (such as the microphysics of cloud nucleation, land surface heterogeneity, convective plumes, and ocean mesoscale eddies) and in the dynamic views provided by remote sensing. At the same time, the process of model development has become more convoluted and involves many more components than it did originally This has led somewhat predictably to an unfortunate reduction in transparency over time. While two of the models discussed (NCEP Climate Forecast System (CFS) and GEOS-5 (Goddard Earth Observing System 5) from NASA GMAO) are primarily used for short-term (daily to seasonal) predictions, there is sufficient overlap with the models focused on longer-term problems (decadal to multidecadal periods) to warrant describing them all as “climate models” below
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