How We Tune Models

How We Tune Models

Summary of the principal components of the radiative forcing of climate change

We started this module with a review of incoming and outgoing radiation. How do we balance the incoming and outgoing radiation to achieve a stable control climate? Usually modelers find a parameterization that has a large effect on the energy budget within the range of observational uncertainty. For example, this graphic shows the change in magnitude of different forcing mechanisms since the start of the industrial era, along with their associated uncertainty.

Based on this graph, which of the following forcing mechanisms would be the best candidate for tuning a climate model? (Choose the best answer.)

The correct answer is (c) Total aerosol-Direct effect.

Among these options, the direct effect of aerosols has a relatively large effect along with a relatively large uncertainty. Parameters associated with the effect can be altered, within the range of observational uncertainty, to achieve a stable control climate.

More typically, modelers choose a parameterization associated with cloud cover. Because clouds reflect solar radiation back to space, the amount of cloud cover strongly regulates the global energy budget. More clouds reflect more sunlight, cooling the Earth. Less clouds allows more sunlight to reach the surface, warming the Earth. We only have rough estimates of the amount of liquid and ice in clouds, the rate at which cloud particles are converted to precipitation, and the impact of clouds on short- and longwave radiative transfer. This allows some latitude in tuning the cloud parameterization to maintain energy balance for a fixed climate model system.

Within the range of uncertainty, parameters within the cloud scheme are adjusted to yield a more realistic energy budget. For example, the rate at which water vapor is converted to cloud water or ice and eventually to rain is not well-understood, with significant uncertainty. If less vapor is eventually converted to rain, then more vapor remains in the atmosphere contributing to cloud formation. Alternatively, more vapor converted to rain tends to dry out the atmosphere, resulting in fewer clouds overall.

Several important points need to be stressed about model tuning:

  1. Tuning is done only within the statistical, physical, or dynamical uncertainty of the parameter. As our understanding of atmospheric processes increases, the uncertainty in parameterizations decreases, making it more difficult to tune models.
  2. Tuning is done to achieve a stable control climate, not to reduce biases in model simulations. We reduce model bias by improving parameterization schemes and/or increasing model resolution.
  3. Tuning is not confined to climate models. Weather forecast models are also tuned, though in a different way.

With weather forecast models, energy balance will have little effect over the short duration of a forecast period. Instead, forecasters may find that the timing or spatial distribution of a specific event is poorly simulated. It could be rainfall amounts or frontal passage associated with storm systems. So, in contrast with climate modelers, forecasters tune weather models to reduce known biases. This is done by experimenting with different parameterizations to identify the source of the bias. For example, model developers may find that the convection parameterization is biased. By changing parameters in the convection scheme, or even replacing the convection scheme altogether, the model may do a better job of simulating a specific type of event.

Both the climate and weather forecasting communities tune their models. They're tuning them to improve them, and the tuning is done within the range of uncertainty that exists in the observations.