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140 kts winds are cat 5. Sent from my Pixel 5a using Tapatalk

JB states or insinuates... 1) The CO2 rise is steady but the warming isn't therefore CO2 cannot be a contributing factor. 2) Seismic activity is correlated with the temperature rise therefore geothermal heat input is the cause of the warming. There is a possible 3rd insinuation related to hurricane activity that I have not yet deciphered due to the strange flow of the article so I won't discuss it. 1) This is an all too common contrarian talking point. In it's generalized formed it is a statement that if variable C is not fully responsible for effect E then it cannot be responsible at all. Obviously this is absurd and a degreed meteorologist of all things should be keenly aware of this flawed thinking. It would be like saying because convective available potential energy (CAPE) is not the sole discriminator of severe weather outbreak breadth and magnitude then it cannot have any modulating effect. Obviously absurd. Furthermore, if he doesn't think CO2 is the cause then he needs to explain where all of that energy that CO2 is blocking from escaping is going if it is not being retained within the climate system. Since JB often uses global circulation models (GCMs) I'm going to assume he accepts that CO2 impedes the transmission of energy since GCMs employ radiative transfer parameterization schemes (like the RRTM) which unequivocally say that CO2 produces a positive radiative force when its atmospheric concentration increases no different than say water vapor. 2) He doesn't explain where he got the seismic data so I cannot replicate his work. But assuming the data is correct and there are no caveats to it's use (experience tells us that is a big IF coming from JB) that still does not mean seismic activity is THE cause of the warming. Correlation does not guarantee causation. Furthermore, we know the planetary energy imbalance is about +0.8 W/m2. It is generally accepted that geothermal activity releases about 0.1 W/m2 on average. If JB is going to make the extraordinary claim that geothermal activity increased by a factor of 9 (0.9 - 0.1 = 0.8 W/m2) then he needs to present extraordinary evidence. Finally, he will need to explain how warming from bottom up can cause the stratosphere to cool.

12Z GFS isn't exactly what I'd call bullish with convection in SE IA, but it isn't devoid of it either. The 12Z UKMET, OTOH, is actually pretty bullish with convection more so even than the 0Z cycle. And the 12Z ECMWF continues to be bullish in this region. I think given that I can't justify an argument against a moderate risk in the IA/IL border area.

CIPS analogs are not as bullish for this event as they were the last couple of days. And based on model runs yesterday and the first ones trickling in this morning (9Z SREF and 12Z HRRR) there seems to be a de-escalation of the severe threat tomorrow IMHO. There are some significant dewpoint depressions (> 12 C) in the current risk area especially in the southern part of it with only isolated elevated 0-3 km CAPE values. With the presence of a strong cap and weak forcing I don't think it is unreasonable at this point to argue in favor of the position that the convection may be sparse and isolated. I realize the severe risk of any hypothetical supercell that does manage to mature could be high, but just how many of them will there be? I have to be honest. I'm even questioning the moderate risk at this point. I'm not saying the SPC is wrong. I'm just starting to think a bit. I want to see if the 12Z ECMWF holds firm with it's more bullish signal on convection.

4 of the top 5 CIPS analogs were high risk days. Edit: The other one was a moderate that produced 36 tornado reports.

Someone correct me if I'm wrong, but... For IL you need to go back to 2013 for the most recent high risk outbreak. For MO you need to go back to 2006 for the most recent high risk outbreak.

And it's not just the GFS. The UKMET, CMC, and ECMWF are all pretty robust. One thing that caught my eye was the minimal overnight junk convection in the warm sector. I mean it's definitely there with some models showing more than others, but it's modest relative to what is typical. We'll see if that trend continues. I was going through the GFS forecast soundings for Missouri, Nearly the entire state generates a PDS TOR sounding at least some time during the day or night.

Relevant...

The IPCC's Climate Atlas could be useful tool for assessing questions like these. Unfortunately I find it difficult to use specifically for snowfall though. The Regional Information map itself makes sense, but when I use the "Point information" tool I get a value in mm/day that doesn't make much sense to me and seems to conflict with other information available. We can say that each incremental increase in global warming results in less snowfall for the northeast and other areas of the US for that matter.

In addition to the Hansen et al. 1981 prediction we also have the IPCC FAR prediction from 1990 that is notable as well. This prediction is summarized in the SPM via figure 5 (pg. xix) for emissions scenarios and figure 9 (pg xxiii) for predictions. I think we could debate the details of which pathway humans chose, but we could probably all at least agree that it was closer to scenario B than scenario A. I think I land more in the camp that we were more likely choose something just slightly under scenario B based on the CH4 and CFC11 emissions. Given that it is more likely IMHO that the IPCC, like Hansen et al. 1981, underestimated the warming since HadCRUTv5 shows 0.66 C of warming putting us just slightly above the scenario B prediction. That's pretty good for 30 year prediction. But the main point here is that contrarian statements that the IPCC didn't get the global average temperature prediction right is farcical.

NOAA STAR just released v5 of their satellite dataset. STAR has been around for awhile but until now has not provided global average temperature timeseries. For that reason I never bothered looking at it...until now. In the past I have been critical of UAH because I have felt it is an outlier. I'm going to temper my criticism based off of what I'm seeing from STAR. One thing I've done in this thread is to compare global average temperature datasets. I should point out that the comparisons I have made above aren't exactly applies-to-applies comparisons since the radiosonde and satellite datasets are not measuring the same thing as the surface and reanalysis datasets. For example, RATPAC is the mean of 850-300mb and RSS/UAH each have their own TLT weightings providing bulk means focused on different atmospheric layers. Not only does this make interpretations of the comparisons above a bit nebulous, but it even makes comparing satellite-only datasets nebulous. What I'm going to do here is apply the Fu et al. 2004 method of deriving a "corrected" TMT product for each satellite dataset. This allows me to make a like-to-like comparison of them. The TMT corrected product is simple. It is FuTMT = 1.156*TMT - 0.153*TLS. Interestingly Zou et al. 2023 produces a total tropospheric temperature (TTT) product that is computed as TTT = 1.15*TMT - 0.15*TLS and nearly identical to the Fu et al. 2004 method. Both methods are designed to remove the stratospheric cooling contamination from the TMT product. Anyway, since all 3 datasets (UAH, RSS, and STAR) provide TMT and TLS products from 1979 to present it is pretty easy to make the comparison. Notice that UAH and STAR largely agree at least according to the linear regression trend. Based on this I have no choice but to consider UAH in a new light. Maybe it isn't an outlier afterall. I'll reserve judgement for a later time and after the experts way in on the STAR methodology. It is important to point out this is a double edged sword. While STAR agrees more with UAH than RSS in terms of the linear trend it does not agree so well with either in terms of the trend after 2002. As Zou et al. 2023 note STAR shows a significant acceleration in the warming even beyond that of UAH and RSS. References Zou et al. 2023 Fu et al. 2004 (paywalled)

Hansen et al. 1981 was pretty close. If anything they underestimated the warming. From 1979 to 2022 I show 0.8 C of warming based off the linear regression trend of GISTEMP.

There is a 4-5 month lag in the global average temperature response to ENSO cycles so I'm not expecting near record temperatures this year. 2024 would be the year to watch for a possible record.

I had a contrarian tell me that I should be assessing model skill based on older model runs a few days ago. So by request I have swapped CMIP5 with CMIP3. I also added JRA and MERRA to the mix. This graphic now contains 10 global average temperature datasets (1 radiosonde, 2 satellite, 3 reanalysis, and 4 surface). Anyway notice that CMIP3 predicted +0.21 C/decade and the multi-dataset composite is +0.20 ± 0.06 C/decade. That's pretty good.

A group of us in a local St. Louis weather forum tried his method a decade ago. It doesn't work...like at all. In fact, if I remember correctly climatology had equal or better skill than the LRC method. That means if scoring by anomaly correlation coefficient it would get a near 0 score. Contrast this with a score of 0.6 which is said to be on the threshold of "useful".

Given the 7 consecutive days of decline I'm thinking it is more likely than not that the melting season has begun.

You mentioned UHI @Roger Smith. I want to address that because it is interesting and because it is one of the most misunderstood topics in the climate debate. First, some definitions. UHI Effect - This is an increase in localized temperatures in urban areas relative the surrounding region as a result of land use changes. It is a phenomenon that has a physical impact. UHI Bias - This is a high or low bias in the measured regional average temperatures caused by defective spatial averaging techniques. It is a phenomenon that has an unphysical impact. It is important that these concepts not be conflated. Ideally we want to keep the UHI effect in regional and global average temperature datasets because it is real. What we don't want to keep in the datasets are any UHI biases. The biases are caused by grid meshing strategies, station moves, station commissioning or decommissioning, urbanization or deurbanization, etc. An example of a high bias is when you have a grid cell is that is 25/75 urban/rural but the station ratio is flipped such that it is 75/25 urban/rural. In this case you are overweighting the urban stations and effectively using them as a proxy for the rural area. An example of a low bias is when the station ratio starts out 75/25 urban/rural for a grid cell where urbanization stalled and then changes to 50/50 urban/rural. In this case you create an artificial low bias even though you may still actually be overweighting the urban area. The timing of urban/rural station ratio and the rate of urbanization (including stalls) affect how the bias plays out in that grid cell. Most people don't take the time to consider that the UHI bias can cause us to underestimate temperature trends. I'm not saying that low biases outnumber high biases. I'm just saying that the sword cuts both ways and if you want to a rigorous analysis then you need to consider how UHIs could cause low biases as well. A lot of datasets deal with the bias by just simple removing the UHI effect altogether. That does mitigate the bias, but at the expense of underestimating the surface warming a bit. I think the reason why this is such an appealing approach is because it is simple and because the UHI effect isn't that much considering only 2% of the planet is urbanized. At the end of the day UHI is such a small part of the global average that it just doesn't matter either way. But I'll leave readers with the Berkeley Earth's analysis. They found that the UHI bias on the global average temperature trend is statistically equivalent to 0.00 C/decade, but that if anything it is more likely to bias the trend too low albeit by an insignificant amount. I think this shocks a lot of people because the thought process is that since the UHI effect is always positive then the bias must always be positive as well which isn't true. [Rohde et al. 2013]

SREF says tornado ingredients are higher today than tomorrow.

We were discussing the Feb. 1960 cyclone in a local St. Louis forum and so I decided to pull the reanalysis for it. It actually looks like a good analog to the current storm.

There are some potent forecast soundings showing up on the 12Z GFS in the OK/TX/AR/LA border area where convection is breaking out.

Antarctic sea ice extent has been up the last 5 days so it is looking more likely that the minimum of 1.766e6 km2 per NSIDC will be the new record.

Here is weather historian Christoper Burt's list of barometric pressure records for each state from his blog. The February 1960 cyclone that Hoosier mentioned is listed frequently.

A 974mb low near Little Rock, AR? What is the deepest a mid-latitude cyclone has gotten in Arkansas?

I was just made aware today that NOAAGlobalTemp was upgraded last week from 5.0 to 5.1. The trend from 1979/01 to 2022/12 increased from 0.172 C/decade to 0.180 C/decade. https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp The big change is the implementation of full spatial coverage. See Vose et al. 2021 for details on the changes. Here is the updated graphic based on preliminary data I had to download manually.