Surface Conditions in Coupled Runs #150
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@megandevlan this is great. And I think it gives hope that 017 is not freezing as LHFLX and SHFLX remains elevated at year 30 much more like 016 than any of the other runs. Could you also plot for the cloud forcings (SWCF/LWCF)? Thanks. |
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Following on the discussion in (#149), I'm taking a similar approach to @gustavo-marques and plotting the mean March fields for years 15-25 in all cases. The surface fluxes in 0017 are weaker in that region of large MLD compared to 018b and 019b: It looks like the near-surface winds (neutral equivalent U10 in this case) are weaker in the 017 case around the southern edge of Greenland, maybe that's factoring in (understandably, looks like largest MLDs are correlated with where U10n is large) In terms of cloud forcing, 018b and 019b bumped back up the LWCF relative to 017 thanks to the tuning, but it's stronger further into the Lab Sea in 018b if I'm seeing this right: |
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Quick look at the global mean surface values with 9 years of run 020 included (020 = 018b but using camdev phys). These are just the annual mean values, but for some reason global mean SHFLX looks like it's unusually low and LWCF is pretty weak as well: |
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In light of large differences between coupled cases, primarily 009 and 016 (all changes we want to include vs. the new control case) @cecilehannay suggested we take a look at the near-surface conditions simulated in CAM as well. As a first pass, here are a few time series of both global annual averages and over the Labrador Sea in particular:
Runs 18b and 19b are similar to 017 but have a more reasonable RESTOM (after some tuning of CLUBB parameters). For right now they're somewhat short, but it looks like this is already having an impact on global mean LHFLX. In the Labrador Sea in particular though, it's interesting to note that after 25-30 years, the SHFLX and LHFLX in case 009 is much lower than in the new control and subsequent tests; so changing the dynamical core doesn't reproduce that behavior by this metric. It'll probably be worth taking a look at seasonal means (not just annual), and maybe looking at some of these differences spatially as well though.
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