ClimaCoupler Lessons Learned
We’ve tried two different approaches to implement remappings:
- Non-conservative clean_mask function applied after non-monotone remapping
- Cuts off values outside of desired range
- Advantages: fast, does not reduce spatial resolution
- Disadvantages: does not conserve global total of quantity being remapped
- Conservative monotone remapping function
- A monotone remapping has the quality that no new minima nor maxima are introduced (i.e. all weights used in the mapping are on [0,1])
- Advantage: remapping is both conservative and monotone
- Disadvantages: decreases spatial resolution, slower approach
TempestRemap has multiple functions that generate remappings. The currently-used function in ClimaCoupler is GenerateOfflineMap, and the alternative is GenerateTransposeMap. Note that GenerateTransposeMap reverses a previous remapping and can only map FV griddings to CGLL (not vice versa). Thus, to apply this function, a minimum of 2 and potentially 3 remappings must be applied, which is quite costly.
To compare GenerateOfflineMap and GenerateTransposeMap, as well as monotone vs non-monotone remappings, we performed a number of remappings using the seamask.nc dataset.
We find that at a spatial resolution of h_elem=6, monotone and non-monotone remappings produce qualitatively similar results when applying the map created by either GenerateOfflineMap or GenerateTransposeMap. However, at a spatial resolution of h_elem=4, the monotonicity has a substantial effect. This suggests that enforcing monotonicity of a mapping may be important at lower resolutions, but is not as essential for slightly higher resolutions.
- Monotone remappings should be used when being applied to quantities where global conservation is important (i.e. for fluxes), but are not necessary for values where this is not required (i.e. for land cover) or when spatial resolution is sufficiently high.
- GenerateOfflineMap is the currently used method to create remappings in ClimaCoupler, and performs about as well as GenerateTransposeMap. In addition, it is easier to use as it doesn’t require a previous mapping, so we will continue to use it moving forward.
