[Liwg-core] Follow-up from today's chat: resetting snow

Bill Sacks sacks at ucar.edu
Tue Mar 28 12:04:08 MDT 2017


Hi all,

Sorry again about joining so late, even after asking that the call be rescheduled to accommodate my schedule....

Here are some thoughts from after the call.

We thought Raymond should continue his run for at least 30 years total. 

The key question I'd like to know is: Are there many points where:

(1) If we start with snow at or near h2osno_max, then we stay at or near h2osno_max (10m) for the whole run

(2) If we start with snow near 0, then we remain only seasonally-snow-covered for the whole run.

If so, that suggests that there truly are multiple equilibria, and initialization is important.

On the other hand, if a column initialized near h2osno_max shows snow is gradually decreasing, then that suggests that it may eventually reach a seasonally-snow-covered state on its own (with enough years of coupled spinup), so we may not need to do this re-initialization procedure. (However, I'm not sure if there's the possibility that the snow pack could shrink to, say, 5m, but then get stuck there, due to very dense snow layers.)


A possible implementation for handling this temporary capping came to mind right after the call, so I wrote it out. It's not 100% complete (and is UNTESTED!), but it did make me see that this wasn't as hard as I imagined it might be... though this still neglects the handling of the mask, which I think is the hardest part (well, building the mask & getting it into CLM at all resolutions is really the hardest part). See attached files – the modified SnowHydrology, and a patch file that can be applied to clm4_5_14_r227. If we decide to just do the snow resetting for tundra points – not glacier points – then the attached implementation is nearly all we need, I think.

Finally, a thought about the mask: I think we should have the mask dataset give something like the topographic height of the equilibrium line – rather than having column-by-column values of true vs. false. This will work a lot better if we want to run with a different number or definition of elevation classes: this mask dataset will be independent of the elevation class division. This could be built by taking the mean topographic height in between the lowest accumulation and highest ablation column in each grid cell (or something like that).

Bill S


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