Timothy Merlis (Princeton): Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change
Yan-Ting Chen (McGill): How low can CO2 forcing go?
Luke Davis (CSU): Influence of pattern effects on the relationship between climate feedbacks and internal variability
09:44:44 From Andrew Williams (he/him) : @Luke Davis , CMIP6 models tend to have a stronger El Nino-like warming under abrupt4xCO2 than CMIP5 models. Do you think this can explain why perturbed feedbacks in CMIP6 look more like the unperturbed ones than in CMIP5?
09:47:16 From Kyle Armour : Thanks for the nice talk, Luke. I’m trying to reconcile your results with the many previous papers who found weak correlations between internal variability feedbacks and CO2-forced feedbacks in CMIP5/6 models and thus couldn’t derive an emergent constraint. Is the difference that you are focusing on SW and LW cloud feedbacks in particular, rather than the net feedback?
09:50:34 From Thorsten Mauritsen : @Kyle Armour yes, there is a lot of compensation between LW and SW, see e.g. Figure 4 in https://doi.org/10.1029/2022GL100075. For net the correlation goes away. It will come back eventually, but you need ~60 years of data (https://www.authorea.com/doi/full/10.22541/essoar.168986237.76892247).
09:52:24 From Luke Davis : Thanks Kyle! Yes I think that’s the main factor — we found inter-model relationships for the clear-sky components are very very weak so if they are included in the regression they tend to obfuscate the relationship. It seems that processes determining spread in late cloud responses tends to be more closely related to processes determining spread in ENSO-like cloud changes
09:52:40 From Mark Zelinka : @Kyle Armour #notallstudies show a poor relationship in the net (thttps://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066698)
09:52:56 From Kyle Armour : Replying to "@Kyle Armour yes, th..."
Thanks! Yes, I had seen the first of those papers and was thinking about that result (which seems consistent with previous papers too). Will check out the new one!
09:54:50 From Thorsten Mauritsen : Reacted to "Thanks! Yes, I had s..." with 👍🏻
09:55:40 From Kyle Armour : Replying to "@Kyle Armour #notall..."
Yes, net cloud feedback seems like it has better correlations than the net feedback. I see Luke points out this is because the clearsky feedbacks have poor correlations.
09:57:58 From Mark Zelinka : Reacted to "Yes, net cloud feedb..." with 👍
09:58:07 From Luke Davis : Replying to "@Kyle Armour yes, th..."
@Thorsten Should also note in our results the relationship/constraint seems holds for the net cloud feedback as well (SW + LW) — however it is somewhat weaker. And clear-sky is near-zero. With our methodology we didn’t find a very strong net-R relationship
09:58:32 From Thorsten Mauritsen : Reacted to "@Thorsten Should als..." with 👍🏻
09:58:56 From Luke Davis : Replying to "@Kyle Armour yes, th..."
And thanks for the papers looks very interesting!
09:59:34 From Clare Singer (she/her) : @Tim Merlis Have you calculated any metrics for convective aggregation to further understand the RH changes you see in XSHiELD? (This is a very exciting result!)
10:00:42 From Yi Huang, Dr. : Nice talk, Tim! Was the instantaneous forcing in xShield run computed? Saw anything interesting?
10:02:54 From Robert Wood : Thanks Andy and Cristi for all their work setting this up.