B. Geerts and E. Linacre |
12/'98 |
Differences with NWP models
A general circulation model (also known as a global climate model, both labels are abbreviated as GCM) uses the same equations of motion as a numerical weather prediction (NWP) model, but the purpose is to numerically simulate changes in climate as a result of slow changes in some boundary conditions (such as the solar constant)or physical parameters (such as the greenhouse gas concentration). Numerical weather prediction (NWP) models are used to predict the weather in the short (1-3 days) and medium (4-10 days) range future. GCM's are run much longer, for years on end, long enough to learn about the climate in a statistical sense (i.e. the means and variability). A good NWP model accurately predicts the movement and evolution of disturbances such as frontal systems and tropical cyclones. A GCM should do this as well, but all types of models err so much after some time (e.g. 2 weeks), that they become useless from a perspective of weather foresight. The quality of a GCM is judged, amongst others, by the quality of the statistics of tropical or extratropical disturbances.
An error in the sea surface temperature by a few ºC, or a small but systematic bias in cloudiness throughout the model, matter little to a NWP model. For a GCM these factors are important, because they matter over a long term. GCMs ignore fluctuating conditions when considering long-term changes, whereas NWP models take no notice of very slow processes.
State-of-the-art GCMs are coupled atmosphere-ocean models, i.e. a model simulating surface and deep ocean circulations is 'coupled' to an atmospheric GCM. The interface is the sea surface: that is where the transfers of water (evaporation/precipitation) and momentum occur. An accurate coupling of the fast atmosphere to the slow ocean (with long memory) is essential to simulate the ENSO, for instance (Note 11.A). GCM's can further be coupled to dynamic models of sea ice and conditions on land. Short to medium range NWP models are usually not coupled to a dynamic ocean model. The GCM-NWP comparison is summarised in Table 1.
Table 1. A comparison between NWP models and GCMs
contrasts |
NWP |
GCM |
goal |
to predict weather |
to predict climate |
spatial coverage |
regional or global |
global |
temporal range |
days |
years |
spatial resolution |
variable (20-100 km) |
usually coarse |
relevance of initial conditions |
high |
low |
relevance of clouds, radiation |
low |
high |
relevance of surface (land, ice, ocean...) |
low |
high |
relevance of ocean dynamics |
low |
high |
relevance of model stability |
low |
high |
time dimension |
essential |
ignored |
similarities |
|
|
physics |
equations of motion (plus radiative transfer equations, water conservation equations ..) |
|
method |
Finite difference expression of continuous equations, or spectral representation; run prognostically |
|
output |
state variables and motion of the atmosphere in 3 dimensions |
|
maximum time step |
controlled by spatial resolution (CFL condition) |
A key problem in GCM (not NWP) modelling is long-term stability, and sensitivity to small changes in surface conditions or radiation input. The atmosphere may be ‘almost transitive’. This means that it is neither invariant (i.e. intransitive) nor transitive (1). An ‘almost transitive’ system can flip between alternative patterns. The flipping to and from Ice Age conditions is an example. An increase of solar radiation will lead to a rising temperature, to an extent depending on the amount of ice on the surface; an ice cover will reflect much of the extra radiation away, causing less heating, until eventually the heating is sufficient to melt the ice. Conversely, reduced radiation will lower temperatures more if the surface is free of ice, accelerating the formation of ice. The difference between the reluctance of ice melting and the rapidity towards ice formation leads to ‘hysteresis’: when there is a difference between the radiation inputs at which ice comes and goes, two distinctly different global mean temperatures can arise under the same intermediate radiation inputs, depending on whether the input was waxing or waning (1).
What GCMs and NWP models have in common...
Reference