Depending on the particular diagnostic, output may consist of instantaneous or time averaged data. Instantaneous data is written out periodically on those time steps that fall nearest to the end of a specified interval. For instance, specifying a 30 day interval means that results are written out every 30 days from some specified reference time. The reference time is also specified through namelist and is the same for all diagnostics.
Averaged data starts out being accumulated over all time steps within a specified interval. It is then averaged and written out periodically at the end of the interval. If the interval were 30 days, results would be output every 30 days from the reference time and would represent 30 day averages. It is possible to produce sub sampled averages by specifying an averaging period as less than an interval. The averaging period is also input through namelist. For example, if the interval was set to 30 days and the averaging period was set to 2.0 days, then 2 day averages would be written out every 30 days and the averaging would be over days 29 and 30 of each interval. How about producing monthly averages when months vary in length? Enabling option monthly_averages will over-ride values specified by ``interval'' and ``period'' and make it so. However, this option is global and applies to all time averaged diagnostics that are enabled. Refer to Section 39.11 for more details.
How often is it necessary to sample model generated data? It should be sampled often enough to resolve the shortest time scale which is of interest. Of course, it follows that longer time scales will also be resolved. For example, if data is sampled at an interval of once per month, the implicit assumption is that time scales with periods of four months and longer are being resolved.
What happens when the data has energy at periods of one week? If data is sample instantaneously once per month, then sampled data will be aliased by the shorter period. One way to remove this alias is to produce a monthly climatology. This is done by saving data for many years and averaging all Januaries together, all Februaries together and so forth to produce twelve climatological or ``mean'' months. The alias error reduces as more and more years are included in the climatology.
A better way is to filter out energy at periods of one week by constructing monthly averages which are then saved once per month. A dataset of monthly averages represents estimates that are statistically more stable than a dataset of instantaneous values. If there is little energy at periods shorter than the period of interest, then saving instantaneous samples is essentially the same as saving monthly averages.
The averaging period need not always equal the interval at which results are saved. This applies when frequencies are widely separated. For instance, suppose that the amplitude of a diurnal period (one cycle per day) is not negligible compared to the amplitude of an annual period (one cycle per year). The diurnal period can be effectively removed and the annual period resolved by saving a three or four day average at the end of every month.