Climate Sciences and Supercomputers
Climate modelling so far concentrated mainly on the interaction of physical components and their coupling, such as fluid dynamics, radiation balance, latent and sensitive heat exchange or wind effects on the ocean. The employment of modern supercomputers, however, permits to integrate also chemical and biological coupling mechanism as well as to investigate the interaction between the climate and the socio-economic system. The ultimate ambition of the international climate and global change research is the development of a complex model of the earth system comprehending all physical and biogeochemical interactions between the atmosphere, the ocean, the cryosphere and the continental biosphere as well as social issues.
Spatial Resolution of Models
The accuracy of climate prognoses is substantially limited by the resolution of the models, respectively the grid size of the cells being the basis for the numerical simulations. A finer grid size results in more accurate prognoses of regional climate changes. But halving the distance between two grid points already produces four times as much grid points and requires for numerical reasons the tenfold compute performance. Therefore scientist had to wait for the new super computer to approach questions such as “Is it going to rain more often in middle Europe?”.
Numerical models often react sensitively to slight disturbances of the initial data. For this reason the reliability of the models is sometimes questioned. The higher the computer performances become the more frequently ensemble calculations can be accomplished: The model computations are repeated many times with slight variations of the initial data so that random results can be distinguished from statistically proved trends.
Long Simulation Runs
Many scientific investigations require simulations of the earth system over a period of several centuries. To accomplish such experiments within a reasonable time frame, many simulated years need to be computed within a day. But even with the fastest computers available today, some experiments need up to more than a year to compute.
Many physical processes occur on quite small scales compared to the spatial resolution of the model grid. Consequently, the affected processes cannot directly be computed.
The formation of clouds is an example for such small scale phenomena. But clouds play an important role in the climate system and can therefore not be neglected in climate models. Instead of an explicit computation of the small scale processes, the cloud physics in climate models is parametrized for the scale of the models's grid boxes: on the basis of a number of other model variables, the approximate cloud cover is derived and used to compute further interactions.
Similiar parametrizations are used for other processes in the climates system.