The modeling of both weather and climate share a deep history and common pedigree based on fundamental laws of physics. The key difference between climate and weather models lies not in the models themselves, but in the questions they seek to answer. Weather models predict how weather will evolve from an initial state for a particular place and time. Climate models project how the statistics of the climate system will respond to changes in external forcing (i.e., boundary conditions).

For the climate system to be in a steady state, the long-term average energy coming in must balance the long-term energy going out. Boundary conditions in climate models affect the way that energy is absorbed or exchanged in the climate system. Boundary conditions are not predicted by the model and must be specified. Boundary conditions include solar radiation, atmospheric composition, and land use.

Atmospheric and oceanic circulation develops in response to the unequal distribution of incoming solar energy across the globe. Climate models have to account for these circulations. To directly simulate processes in Earth’s climate system, models use a set of equations that balance forces acting in three dimensions and conserve mass and track the temperature of each grid layer. These are the resolved processes.

Processes that operate on a scale smaller than the model grid must be parameterized. That is, their effect over the entire grid cell is given by a single value. Examples include the latent heating due to cumulus convection or the radiative transfer of solar and longwave radiation.

The components that go into a climate model include an atmosphere model, ocean model, land model (including snow and land ice), and sea ice model. A coupler manages the interactions between the different components, accommodating different grids, resolution, and time steps.

When we simulate the climate system, we want no intrinsic climate drift in the model. In a process akin to calibrating laboratory instruments, modelers “tune” the model to achieve a steady-state. To tune a climate model, modelers vary a parameterization that has a large effect on the energy budget within the range of observational uncertainty.

Climate models can be tested in several ways.

  1. We can develop skill scores, which reduce model biases to a single number.
  2. We can examine the spatial and temporal distribution of biases in model means.
  3. We can compare the natural variability in the simulated climate with that in the observed climate.

In the future we expect that increasing computational capabilities will allow models to be run at increased resolution and complexity. As model complexity increases, more parameters become predicted, rather than prescribed. The eventual goal of climate and weather modelers is to run the most complete models at high resolution with the most accurate parameterization schemes.

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