Closed allibco closed 3 years ago
Asked Brain M. about time dimension:
When I’m looking at these, I think using annual averages (and then averaging them up for multi-year runs) is the best way to go. There are science applications where looking at individual months or seasons makes sense, but for validating the climate in a general sense, I think annual means are the appropriate starting place. Once you have annual averages, I think looking at both the time series and the long-term (>10y, preferably >30y) average are useful. Does that help?
Usually the months are all given the same weight, even though it would be better to weight them by the number of days. At least, I am pretty sure that is how seasonal/annual averages have been done with NCL in the AMWG diagnostics in the past.
Figure out how to add checks for derived variables.
Examples from Julio B. for CAM:
The budgets I'm thinking of are pretty basic. The most basic is the top-of-model radiation budget "RESTOM" which is just
RESTOM = SUM_ij ( ( FSNT(i,j) - FLNT(i,j) )* area(i,j) ) / SUM_ij( area(i,j) )
where FSNT and FLNT are the CAM history variables for net top-of-model shortwave and longwave radiation fluxes. The area(i,j) is a variable that describes the horizontal area of each model grid cell in some unit. Sum_ij just means a horizontal sum. This could be a one dimensional sum for native output from some dycores.
Another important global mean variable is the mean global precipitation
global_PRECIP = SUM_ij( PRECT(i,j) * area(i,j) ) / SUM_ij( area(i,j) )
PRECT is the CAM variable for total precipitation. Sometimes it has to be constructed as the sum of large-scale precip PRECL and convective precip PRECC , i.e., PRECT=PRECC+PRECL.
Anyway it would be good to know how much (%-wise) compression affects these totals.