...
Note that the cell integrated methods have significantly higher errors as compared to bilin. This is in part due to the fact that the target grid is coarse, and the error is computed as a pointwise error at cell centers (as opposed to the harder-to-compute cell averaged areaerror). To check the performance of cell-integrated algorithms without this issue, we used maps to a higher resolution atmosphere where the difference between pointwise and cell-averaged error will be smaller, we ran the test with maps from a coarse to fine grid:
mapping error for the Y16_32 test function IcoswISC30E3r5 → ne120pg2 | |||
|---|---|---|---|
Algorithm | L2 Error | Max Error | num points with missing ocean data |
aave | l2=3.636e-04 | max=1.850e-03 | 0 |
fv2 | l2=5.972e-05 | max=5.959e-04 | 0 |
ESMF bilin | l2=1.232e-04 | max=4.769e-04 | 9315 |
TR bilin | l2=1.231e-04 | max=4.770e-04 | 5809 |
TR intbilin | l2=2.272e-04 | max=2.325e-03 | 0 |
...
The 4th column in the above gives the number of points on the atmosphere grid, which are fractional ocean points (as determined by the aave map), but will receive no data from the ocean->atmosphere map. Maps with such points are unusable in E3SM at as ocn->atm statemaps, because the atmosphere will be expecting ocean data at these points but instead will receive 0. As one of the main state variables it Temperature, this will cause the model to crash with unphysically cold temperatures, or create speckling patterns of stationary low surface temperature ( add link herei.e.: /wiki/spaces/NGDNA/pages/3597565953 ).
Note that all the cell integrated TR algorithms are consistent with the aave map. This is because they use the same overlap mesh to compute the mapping weights. Thus we could, for the first time, consider using these higher order maps in E3SM for mapping state variables from ocean to atmosphere.
...