From Dr. Roy Spencer’s Global Warming Blog
August 2nd, 2022 by Roy W. Spencer, Ph. D.
The Version 6.0 global average lower tropospheric temperature (LT) anomaly for July, 2022 was +0.36 deg. C, up from the June, 2022 value of +0.06 deg. C.
The linear warming trend since January, 1979 still stands at +0.13 C/decade (+0.11 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).
Various regional LT departures from the 30-year (1991-2020) average for the last 19 months are:
YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST
2021 01 0.12 0.34 -0.09 -0.08 0.36 0.50 -0.52
2021 02 0.20 0.32 0.08 -0.14 -0.66 0.07 -0.27
2021 03 -0.01 0.13 -0.14 -0.29 0.59 -0.78 -0.79
2021 04 -0.05 0.05 -0.15 -0.28 -0.02 0.02 0.29
2021 05 0.08 0.14 0.03 0.06 -0.41 -0.04 0.02
2021 06 -0.01 0.30 -0.32 -0.14 1.44 0.63 -0.76
2021 07 0.20 0.33 0.07 0.13 0.58 0.43 0.80
2021 08 0.17 0.26 0.08 0.07 0.32 0.83 -0.02
2021 09 0.25 0.18 0.33 0.09 0.67 0.02 0.37
2021 10 0.37 0.46 0.27 0.33 0.84 0.63 0.06
2021 11 0.08 0.11 0.06 0.14 0.50 -0.43 -0.29
2021 12 0.21 0.27 0.15 0.03 1.63 0.01 -0.06
2022 01 0.03 0.06 0.00 -0.24 -0.13 0.68 0.09
2022 02 -0.00 0.01 -0.02 -0.24 -0.05 -0.31 -0.50
2022 03 0.15 0.27 0.02 -0.08 0.22 0.74 0.02
2022 04 0.26 0.35 0.18 -0.04 -0.26 0.45 0.60
2022 05 0.17 0.24 0.10 0.01 0.59 0.23 0.19
2022 06 0.06 0.07 0.04 -0.36 0.46 0.33 0.11
2022 07 0.36 0.37 0.35 0.13 0.70 0.55 0.65
The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for July, 2022 should be available within the next several days here.
The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:
Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt
I would read the record as showing no real increase since 1997.
The trend since 1997/01 is +0.12 C/decade.
In hundredths of one degree C no less!
🤣🤪
FWIW, Excel reports it verbatim as 0.115090366617479 C/decade with the full IEEE 754 digits.
I know.
I sure felt that last 0.0000000345 C anomaly at 1800 hours on 24th July.
I called 911 about it.
Yes, OMG, we’ll die from +.36C!!!! Destroy our economies. food production and societies to save mother GAIA!
WOW!
That means we emerge from the coldest time the planet has been in, without being in a full blown ice age, at 1.2ºC per 100 years.
A bit slow for my liking. A bit like you really…………
The UAH Global Temperature you’re talking about is air temperature from above the surface up to several km. Not the same height as ground based weather stations & not using the same thermometers (uses different methods & conversions). Notice if they say LT, MT, UT/LS, total troposphere, 2m alt surface or follows land height.
It can be compared with other lower-troposhere data but they can use different definitions & methods. It has some relationship with actual 1m surface temps but thermal gradients occur. The models say the troposphere will warm more & this then causes warmer surface temperatures (less cooling of surface). If surface temps have warmed more than troposphere has warmed then it doesn’t support the models. It’s possible that the surface is warming the air from urban heat Island & change of vegetation effects.
Then you would be reading it wrong. There is no “real” increase since 2016. The period from 2016 appears to be about 0.2C warmer than 1997-2016.
2016 was the peak of the last big El Niño, which brings naturally warmer conditions; so no surprise there.
This July figure, the second highest in the UAH July record, has come during a sustained and continuing period of La Niña, when natural cooling influences are sopposed to hold sway.
I agree the Southern Hemisphere and Tropics are warmer than you’d expect as the La Nina comes to an end.
The trade winds are roaring across the Pacific and it remains cloudless though so odds are trending towards another La Nina forming in the fall.
Christopher Monckton of Brenchely
If you only looked at the trend from Mt. Pinatubo in 1991 to the 1997-98 El Nino, we should have alligators in Canada by now. Speaking of which, when was the last major volcanic eruption? Ah, natural variability…
The last major eruption was Tonga only a few months ago, but it was rather unique. It is unlikely to have a major impact on the UAH temperatures even though by some estimates it was at least as explosive as Krakatoa. You have to go back to the Pinatubo 1991 eruption as the last major eruption to significantly perturb UAH temperatures.
bdgwx, we have had the Tongan aerosol plume over NZ & Australian skies since the end of the 1st week in April. This has spread around the SH, even down to Antarctica. From photographic records the impact in our skies is far greater than anything I have seen,except when the Pinatubo material parked over us 31 years ago this week. The vivid, long-lasting twilights have been noticed more by lay people than any other such incident since I started into amateur astronomy 49 years ago. The upcoming Total Lunar Eclipse in 3 months’ time, should be our best indicator of what impact the Tongan eruption is really having on our atmosphere. The first “Black TLE,” that I saw was Dec 30th, 1982, the year El Chichon in Mexico & Mt Mayon in the Philippines, erupted. Those eruptions coincided with a particularly bad El Nino for us here in NZ. The Pinatubo eruption also happened before a moderate El Nino in our austral spring. Summer was short that year, and again the following year. 1992 was also the dullest year on record for NZ. Just how much impact Hunga Tonga has on the upcoming La Nina during our spring/summer remains to be seen.
Based on what I’m seeing the Pinatubo perturbation on the UAH temperature peaked about 9 months after the eruption. Tonga was almost 9 months ago and I’m not seeing any warming in the UAH TLS layer.
The Tonga eruption was in January. Seven months ago is more than a few. That would be several.
There was an up tick of volcanism 2008-2011 that co-occurred with the minimum of the 3rd cycle with 8:VEI 4, 1:VEI 5, and 3:VEI 3 eruptions. A total annual average VEI eruptions of 3 or higher =11.8, 2012-2022 10: VEI 4, 11: VEI 3 and 1:VEI 5 A total annual average VEI eruptions of 3 or higher =7.8.
2008-2009 demonstrated the bulk of those VEI>2 eruptions with four VEI 4 eruptions in about 18 months. So with the minimum and the uptick in volcanism in 2008-2009 there is a eyeball correlation the anomalous cooling event that took place then.
Atmospheric So2 is a good proxy. I am a weather/climate hobbyist, I am not sure if So2 is being measured and monitored, nor have a seen any comparison with atmospheric temperature trends. The vast majority of eruptions are submarine and most of them go undetected. So2 does enter the atmosphere from submarine eruptions to some degree.
Increased submarine volcanism would also corelated with increased regional acidification of sea water.
I meant 23 solar cycle not 3rd.
Here’s a paper on trends of sulfur aerosols basically starting at Pinatubo.
https://www.nature.com/articles/s41598-018-37304-0
No volcanic influences that show up over the decreasing emissions in the developed world.
Thanks Robert. Very interesting article. At glance there was no mention of measurement methodology. I would be good to know the name of the satellite program if that is the methodology. The inter-model variability graphic strongly suggests submarine volcanism as the source for most of the SO2 since the densest concentrations are where the most submarine volcano’s exist as well as millions of submarine SO2 vents. What we need now are regional studies of SO2 concentration variability and a wide variety of weather variables (atmospheric temps, etc). It would also be interesting to see the relationship with gravitational variance (mountain vs magma) and SO2 at various levels of the atmosphere as well as temps. Throw in regional sea acidification just for fun. I have no idea to what degree volcanism is variable. The descent of So2 in most of the graphs, (if valid) may indicate a period of greater volcanism during the grand solar maximum of minimum of SC 20 . Pointing to one VEI 5-7 event is pointless given all the sulfur vents around the globe. We do know a 7 or greater event will suppress temps for up to a decade and several of them back to back is catastrophic.
I assume Lord Monkton will confirm but by my calculation the pause is still 7 years and 10 months.
From October 2014 to July 2022 inclusive the trend line is slightly negative.
f(x) = -0.0016243681x + 3.5060790649
That’s what I get too, but as Bellman pointed out in another post it’s possible CMoB will take the 7yr11mo value and round down to 2 decimal places thus yielding 0.00 C/decade and extending the pause by another month.
That would work for me. I am only a beginner at the game and am quite happy to bow to his lordship’s superior experience.
Make that three decimal places.
since Sept 2014 the slope is 0.000297775
Rounding down gives 0.000
That should be 0.00 ± 0.57°C / decade.
It wuz the two day heat wave we had in the UK wot dun that……….
The funny thing about the “climate meltdowns” The Team™ & the MSM
make such a fuss about are actually situations where oceans are venting
stored heat, which when gone, is no longer there to create even bigger
meltdowns in the future. This is the real global cooling that global warming creates. Talk about being “half-bass-ackwards”!
Funny how a heatwave is any hot period longer than 5 minutes these days
All of us in the UK old enough to remember will never forget the long hot summer of 76
I doubt very much anyone will remember the “long hot summer” of 22 without the warmunists constantly reminding is of the hottest temperature evahhhhhh
I have been working on a simple model to predict UAH monthly anomalies. The model has 3 components: CO2, ENSO, and volcanic AOD (from Sekiya et al. 2016). The RMSE of the model is 0.13 C. Based on this simple model I’m expecting the next months to fall into a 1σ range of +0.01 to +0.27 C. The model was predicting +0.17 C for 2022/07. That was an error of 0.19 C and a bit anomalously high being outside the 1σ envelope.
The problem always remain the same. You put “Model (no CO2)” for comparing. However if CO2 is a feedback of another process that controls temperature (from the hundreds available) in the same proportion, you are assuming cause/effect wrongly. That also works only since 1979 (why this starting point?). How does the model perform before that, from 1941 to 1979 when CO2 and temperature were not coupled or had an inverted tendency? How does is perform in all the warm periods before present (Roman, Miinoan, Holocene Highstand – “much warmer than today according to hundreds of peer review papers with modest amount of CO2 in the atmosphere-, etc.). CO2 seems to be a little forced here as in most models…. Why also ENSO only? For recreational effects, try to put AMO there and probably you will have the surprise of being able to skip CO2 completely. They have different periodicity and it’s important to see if they are in phase or not and the respective latency.
Much simple than yours, HADCRUT4 global mean and ESRL AMO index detended by -1. And I don’t have to make the X scale artificially start in 1979 or even consider with or without CO2. AMO is an important climate factor for sure.
The detrend operation with a -1 parameter artificially injects a 1 unit rise in the timeseries. In other words, the AMO trend is actually near zero, but you changed it to rise by 1 unit over the time range. Hopefully this graphic illustrates what is going on visually.
That’s the Earth’s temperature profile.
No. It is the North Atlantic sea surface temperature profile.
I’m… finding a bit hard to believe that J N didn’t do this intentionally.
Of course I did. It gives a better sense of relationship.
After all, the artificial “detrend” also occurs in the “milked” data, as we learned from climategate. “Refresh the past and heat the present”!!!
Excellent, thank you for saying the quiet part out loud and admitting to intentional dishonesty. That is somewhat refreshing.
Why the starting point of 1979? According to bdgwx, the model is intended to predict UAH temperatures, which coincidentally started in 1979! Nowhere in his comment did he say he was trying to predict anything but.
Unfortunately UAH only goes back to 1979 otherwise I would have extended it further back. I’m completely open to adding a 4th (or more) components. AMO is a good idea. I’ll see if I can drive the RMSE down further with AMO when I get some time.
Try to take off CO2 and then put AMO instead. I’m honestly curious about the result. Don’t forget to include me if it works and you publish this 🙂
I did play around with this. Unfortunately no matter how I trained the model adding AMO as a 4th component only increased the model error. And the more weight I gave AMO the worse things got. Keeping only 3 components but replacing CO2 with AMO was catastrophic to the model no matter how I trained it. If someone else wants to take a stab at it I’d be interested in seeing if you can beat an RMSE of 0.13 C.
You need both the AMO and the PDO to get a valid result.
Exactly (and probably hundreds of other known and unknown forcings) 🙂
Richard M said: “You need both the AMO and the PDO to get a valid result.”
I removed CO2 and included AMO and PDO in the model. The base RSME is 0.21 C. Adding any weight to AMO and/or PDO increases the RSME above and beyond 0.21 C. In other words, all combinations of AMO and PDO actually do more harm than good.
How did you normalized data?
JN said: “How did you normalized data?”
The model is of the form M = a*A + b*B + c*C + … + z*Z where A-Z are the components and a-z are the coefficients.
If you have ideas on how to drive the RMSE below 0.13 C let me know and I’ll try it out.
Try to normalize data first in the A-Z components. otherwise some of them will have a different (potencial artificial) weight in the RMSE because the data range is very different, since you multiply them by a weight coefficient (unless you try to balance data with coefficients). See that AMO data tends to have a wider short range than temperature anomalies. If you increase the coefficient beyond 1, for instance, due to the wider range values, the RMSE tends to increase a lot. In this case, if data is not normalized, at least the coefficient must be smaller, bellow 0.7 or so to compensate data variance.
The no co2 model is hilariously bad. This can only be created by assuming we know what the tcr is of increasing co2. Nobody knows this. I eyeballed your graph and the difference in temperature appears to be about 0.7c over the period 1979 to 2022 when co2 levels increased by 25%. This is an incredibly high tcr figure which you have programmed into the model..
The model implies a TCR of 2.0 C for 2xCO2. That is the value that minimizes the RMSE of the model as part of the training step. The high TCR here is partially the result of only having a 3 component model. If I added in CH4 and other GHGs I believe this value would get reduced to around 1.5 C. I’ll trying adding more components to the model to better isolate CO2’s impact as I get time.
Thank you for admitting that the tcr figure you are using is ridiculously high. Of course, this means that your no co2 model is worthless.
I wouldn’t call the no-CO2 worthless. It’s RMSE is 0.21 C. It’s just that the full model is better with an RMSE of 0.13 C. Interestingly, replacing CO2 with AMO yields a minimum RSME of 0.21 C with no AMO weighting. Then as I add more and more weight to AMO it gets worse and worse. That means AMO provides no useful skill whatsoever to this model. It is literally worthless.
Are you sure? Using Temperature and AMO (both normalized), from 1979 to present, the data seems to have a better correlation than your model. Check it out. This time I did not detrend anything (to avoid confusion)!!! You need to normalize all you data to compare apples to apples in any model. See if AMO does not correlate very well with temperature!!! We don’t need to be very wise to see that there’s clearly a relationship, a lot better than with CO2. Of course that cause/effect direction is unknown, as with CO2 and several other factors.
JN said: “Are you sure?”
Nope. It turns out I had a typo when I copied the column in Excel. Here are the models and their root mean squared errors.
M = -0.35 + 0.13*ENSOlag5 + 0.65*AMOlag3 – 5.3*AODvolcanic gives an RMSE of 0.160 C.
M = -0.35 + 2.0*log2(CO2) + 0.13*ENSOlag5 – 5.3*AODvolcanic gives an RMSE of 0.135 C.
M = -0.35 + 1.6*log2(CO2) + 0.12*ENSOlag5 + 0.16*AMOlag3 – 5.0*AODvolcanic gives an RMSE of 0.127 C.
So adding AMO does give me an extra 0.008 C of skill. But replacing CO2 with AMO causes me to lose 0.25 C of skill.
Try to train the model with longer series. I guess that 1979 to 2022 gives a very short interval of data to determine appropriate coefficients. Use HadCru4 global instead, for instance. What package are you using for training the model?
I wrote my own training code in C#. I use a simple recursive descent strategy for optimizing the coefficients and the monthly lags. For example, the training says UAH lags ENSO by 5 months and AMO by 3 months. I’ve not seen independent estimates of the AMO lag, but pretty much everyone agrees with the 5 month ENSO lag which gives me confidence that the training is working correctly. I also did manual training in Excel and got similar results.
Instead of just volcanic aerosols you need to factor all aerosols, which have greatly decreased in the satellite era.
Yep. Agreed. That’s my next task. It’s really hard finding monthly AOD data.
Science is the skill of observation.
Observation does not include taking wild guesses into the future. It might inform the next hypothesis, but no more.
Anything else is mysticism.
I am mystified by your post.
Science is the skill of observation AND prediction. It is the later I’m focused on here. I’m leaving it up to UAH to do the observation. I want to be able to predict the UAH TLT value before it is published and to make long term scenario based predictions as well. I’m pleased with the result so far, but if anyone knows how to drive the RMSE below 0.13 C I’m more than happy to experiment with ideas.
Can’t wait for a new update on the Monckton Pause™. Of all the pauses it is the pausiest.
Surely this one signals the end of global warming.
The pausicity of pauses indicates no significant cooling, yet despite the increase in CO2 the warming rate is not increasing. This indicates CO2 stops working soon after being emitted and new more abundant CO2 emissions are required to maintain warming. Increasing levels of CO2 should result in increasing rates of warming and they don’t. This is a problem for the theory.
Hmmm it looks to me more like no warming is occurring at all, and instead all that’s happening is a series of pauses being stacked one on top of the other. In fact most of the pauses are coolings, so we can suppose some kind of anti-cooling is taking place among the stack of pauses to give the appearance of a long term warming trend. Simply remarkable.
The action of counteracting forces on climate results in climate change being quasi-periodic (oscillatory). Pauses are just a part.
The warming of the world is the result of a decrease in the cooling rate during pauses, while the warming rate does not increase during warming periods. It can be seen very clearly in this image from the UK MetOffice.
This is not how CO2 is supposed to act. It should also increase the rate of warming during warming periods leading to accelerated warming. That is not what happens.
This is what I jockingly referred as “pausity of pauces.” Cooling periods are not what they used to be.
Incredible, global warming is caused by pauses in the rate of non-warming. It’s all pauses all the way down.
Not to mention anti correlation during the man-made global cooling apocalypse period before the satellite data started.
Careful with the late eighties pause, there was a lot of volcanic ashes in the stratisphere then, followed by a huge El Niño event.
Remember all the scaremongering here in England when we had our mini heatwave and that it was a sign that the climate was in meltdown. So surely that would mean that this year’s July mean daily maximum temp would be at a record high?.
Well not quite.
Both the July of 1911 and 1921 amongst others have recorded higher mean daily maximum temps then July 2022. So looking like England’s climate is not going to burn just yet.
Here is US and Global graphically for 2021-2022 YTD.
CMIP model average lower troposphere temperature trends 1979-2014 is roughly 0.28C/decade (0.18-0.41) so something has gone horribly wrong in model land. The policy approaches, attribution studies, and mass-hysteria have no basis. For every month that chugs along at 0.13 decadal trend puts another nail in the coffin of a science run amok. The upward trending temperature is merely an illusion in support of the hypothesis of CO2 + positive feedbacks. The hypothetical mechanisms are not supported in the data.
The models are running way too hot. The theory is plainly flawed. How much longer must this go on? What is the point of science if this is simply ignored?
See my guest post here years ago, “Why Climate Models Run Hot”.There is a very specific reason why they run hot, and it is technically not possible to fix it in the foreseeable future. It has to do with grid sizing and the CFL constraint on numerically solved PDEs.
The gridded GCMs are thus a fools errand. The wrong tool for the job with no hope of success.
A crutch to lean on, and a veil to hide behind. An excuse.
There is no need for such monstrosities – their outputs appear to carry little value. How many more careers will be wasted on such nonsense.
“A crutch to lean on, and a veil to hide behind. An excuse”
A prop controlled by “scientists’, used to scare people.
You are pointing out symptoms.
The root cause:
Climate computer games run hot because
that’s what the people who own the models
are hired and paid to do. Scare people.
Computers “forecast” whatever they are
programmed to forecast. The climate computer
game forecasts are not accurate because
accuracy is not a goal.
Even If the models were perfect,
the climate zealot government bureaucrats
would add a fudge factor to double or triple
the model-predicted warming rate, and that
scary prediction is what the public would see.
That is what the bureaucrats are paid to do.
“CMIP model average lower troposphere temperature trends 1979-2014 is roughly 0.28C/decade (0.18-0.41) so something has gone horribly wrong in model land. “
Well, RSS gets a trend of 0.212 C/decade, so something is a bit shaky in MSU-land.
Satellite avg 0.15, Reanalysis avg 0.13, Sonde avg 0.16, Model avg 0.28
I think the slight warming was in the cards but over the last couple of weeks the ENSO 3.4 temps took a substantial drop, Greenland hasn’t lost much ice this melt season and the jet has been pretty wavy for the summer pushing warm air north. As the NH season transitions to cold we might see a big change occur quickly. The arctic sea ice has hung around which will feed cold air south and as it hits the warmer ocean areas look out for the huge storms that will develop at the boundaries.
ENSO Cooling:
Setting aside the green house gases as a hypothetical variable in atmospheric temperature for the sake of looking at other factors both natural and anthropomorphic. Natural factors: Solar variation and it’s possible dynamic with the earth’s core, Solar variation and impact of cosmic radiation on Earth’s atmosphere, Orbital variation, Variation of Cosmic radiation and it’s impact on mantle heating and stability, Volcanism…etc. (fill in the blank), Is it possible that there are anthropomorphic variables coming into play, (i.e.. Increasing surface area of pavement, roofs and buildings, industrial heat exhaust, heating from lighting, AC/heating Exhaust). These anthropomorphic factors definitely have an impact of the validity of placing temperature sensors. They definitely have an impact on the increasing warming trend in regional megalopolises like the USA East Coast. I have no political soap box here… just an honest question. Is the magnitude of the heat generated by megalopolises a possible factor in atmospheric temperature trending ever so slightly higher? I am not going to preach agrarianism or any other lifestyle remediation. Humans are part of nature and so we deserve to live on the planet in the numbers that naturally flow from human civilization. I actually doubt that pavement and buildings actually do have a global atmospheric temperature impact due to the sheer magnitude of the planet….but how can I be sure. I am also aware that there is no way to measure it. But I think it is a question worth asking. The advancement and application of cooling TEGS could be a economical way to extract waste heat in the megalopolises and make them far more livable, (thinking of London and Philadelphia during heat waves). Stewardship is a thing. The better stewards we can be, the greater the carrying capacity of the globe and the more baby booms we can have so we can be a happier people.
Of the earth’s surface area 130 billion square feet, 0.20%-0.25% is paved or buildings. Asphalt absorbs 90% of solar heat. The heat island effect on global atmospheric temperature… anyone out there know the answer or have data?
Getting here late. The problem is that the surface temperature stations measure a large part of that 0.20 – 0.25 percent. The remaining 99.75 – 99.80 percent is largely unmeasured. You be the judge of whether that provides an accurate picture of the globe.
How about changes in clouds and water vapor?
And biased measurements / adjustments / infilling
that create fictional warming trends ?
Perhaps more important:
Random variations of a complex system.
Random variables are variables we either can’t measure or don’t understand their relationship to other variables. Clouds and vapor are a variables related to the variability of cosmic radiation, and possibly volcanism and other variables either I myself or no one is aware. I don’t know anything about the veracity of Satellite atmospheric temperature measurements. I would assume they are far more valid and reliable then ground based temperature senor placement as we have seen in recent posts. I don’t buy the anthropomorphic climate change/global warming greenhouse gas paradigm because it was a political strategy from the beginning and has been leveraged by everyone. This is why there is so much propaganda because all sorts of strange bedfellows are pumping money into it. Yet, the anthropomorphic variable may still be in operation apart from the consumption of fuels that produce so called green house gases. All I am doing is asking the question about the rapid advancement of pavement during the Massive Global economic expansion of the last 30 years and it’s impact on atmospheric temps. Just pointing to a possible relationship between two variables.
Al Gore politized global warming in order to try to reduce fossil fuel consumption at a time when oil prices had plummeted after Dessert storm with OPEC increasing supply, (remember gas prices 1989 $1.69 by 1996 76 cents) because we aligned ourselves Israel and Saudi Arabia against the Shiites in Irag and Iran and promised to protect Mecca from the Shiites, (the iatrogenic effect was to piss off radical Islam by having a heathen force in the land of Mecca which resulted in Osama bin Mohammed bin Awad bin Laden our friend against the Russians in Afghanistan turning against us)..The false mantra of the 70’s and 80’s of a limited oil supply had been over turned and it was clear that the supply of oil was totally political. So the radical environmental movement called climate change was anti-fossil fuel from the beginning. Other than a lack of unequivocal scientific proof, the green house gas hypothesis always had an anti-fossil fuel political agenda. The movement got started with an extremely naïve conviction about the advancement of tech that would empower an all renewable electric energy system. Some of it was motivated by promoting stocks and making money and selling books. It started off as a dream and become a political leverage point for everyone from Rad left, energy companies looking for deeper leverage over legislation due to them being a greatest tax contributor via carbon taxes and then the threat fear aspect of the movement triggered self- righteous pseudo-religious impetus took hold and drove the movement to the fever pitch that it is now with no sense of reality testing what so ever.
Why is this even a thing? Trying to catch the way our climate evolves as a change of the average difference of all temperatures now and an average 30-year baseline in the past is such a gross oversimplification that it is meaningless. Opposite extremes could cancel out in the average and we would know exactly nothing and still have a problem. This is a nonsensical discussion. It proves exactly nothing on either side of the climate debate.
I wouldn’t be too concerned by this outlier. Also happened back in January 2013. The values went .00 .31 -.03 over a 3 month period. I never saw any explanation for the big jump and then fall. Made no sense then, makes no sense now.
I expect temperatures will drop back, but I don’t seem much of a similarity with January 2013. Consider the year before January 2013
and compare with those leading to July 2022
January 2013 seems to be far more of an outlier than July 2022.
I didn’t check on whether any CMEs or other space based events took place. I’ve seen warming after CME events in the past, but not all of them.
That is a huge increase in energy leaving the planet which can’t be sustained which is why It will go down next month.
Freezing in Australia in July.
The UAH for Australia did not seem to match the reality of on ground temperatures.
Interesting to see if there will be a fall in land based temp measurements.
Agreed. I think it is likely August will drop.
The earth is suddenly spinning faster, since circa 2020.
https://nypost.com/2022/08/01/scientists-baffled-as-earth-spins-faster-than-usual/amp/
This suggests build up of ice at the poles.
According to the article you cited:
“On June 29, the Earth’s full rotation took 1.59 milliseconds less than 24 hours — the shortest day ever recorded.”
Please note that a millisecond is one thousandth of a second. There are 60 x 60 x 24 x 1,000 = 86,400,000 milliseconds in a day. So the shortest day ever recorded was a mere 86,399,998.401 milliseconds. My how time flies.
Baffled scientists. Now there’s a meme for you.
What’s the margin of error?
Global 400m depth-averaged Temperature (bom.gov.au)
Global 150m depth-averaged Temperature (bom.gov.au)
I have put 2 links to BOM Australia global ocean temperature maps for 400m and to 150m.
Go to the bottom of the pages for the anomalies.
It seems to me that the heat from the oceans has moved to the ocean surface and evaporated from there into the atmosphere causing the recent atmospheric warming.
These maps were published August 1st.
This was the second warmest July in the UAH data set, with only 1998 being slightly warmer.
All of the last seven July’s have been in the top ten.
That is expected.
It’s called a warming trend.
That’s what warming trends do.
Until they end.
Which every warming trend does.
Ar least so far on this planet.
That’s a relief.
If a warming trend never ended,
we’d end up with infinite temperature.
But many claim this warming trend,
ended long ago,
or never even started.
Warm equatorial surface water has been replaced to the North, causing more water vapor (our main greenhouse gas) in the air, resulting in a higher temperature of the air. The NH is susceptible to warming stimuli. More water vapor over Land also enhances temperature.
Think about H2O, the main molecule.
0.3°C in 1 month. At this rate we’ll be fried by Christmas.
According to my prediction, which took 24 years of tedious study, by this time next Summer (2023), it will be so hot that a bald man standing outdoors in the mid-day sun, for 15.628 minutes, will have such a hot scalp that one could fry an egg on his head.
25 years ago, in 1997, I predicted: “The climate will get warmer, unless it gets colder.” So my prediction accuracy rate is 100%. And I’m still waiting for a Nobel Prize. Or at least a Nobel Prize participation trophy and a cigar.
Here are the UAH LT globe data plotted with generous expanded uncertainty limits of ±1.4K, along with the residuals of the least-squares regression fit:
This is a plot of the area of UAH grid points in km^2 as a function of latitude; the circumference of lines of latitude decrease from the equator (~40000km) down to zero at the poles as the cosine of the latitude. Because the UAH spherical grid is a constant 2.5 x 2.5 degrees, the area of the grids is considerably larger near the equator relative to the polar regions (note that UAH does not report data for the last three highest latitude points). At 70° latitude, the grid area is 1/4 that of the equator, and 1/10 by 80°.
Because the NOAA satellites are in polar orbits, grid sampling between the polar and equatorial regions differs greatly. Polar grid points can be sampled multiple times each day of the month, while at the equator as many as three days can elapse without sampling.
Here is a different style of analysis that doesn’t use a 30-year anomaly subtraction. Instead, I took the gridded UAH monthly average files, which have the anomaly already subtracted at each point, and subtracted the monthly averages from a baseline year that I selected. I chose 1980 as it was close to the beginning of the NOAA data collection and was close to neutral ENSO for the whole year. The 1980 subtraction cancelled the anomaly subtraction.
After the 1980 subtraction, I then plotted an intensity map using one pixel per grid location, for a total of 72×144 locations. This showed how any given month varied from 1980, which only rarely exceeded ±10°C. I also made histograms for each month, and then assembled them into an animated GIF, see here.
Here is the GIF:
With the histogram data, it is possible to do trendology in several ways. One is of course just the monthly mean difference versus time, but the same can be with the median of the difference, and the histogram peak locations. Here are all three methods plotted, separated by 10°C for clarity. 1980 is blank because this is the baseline year. The standard deviations of the means are shown as one-sigma error bars, and are about ±1.7°C.
Notice that the slopes of the linear regression fits vary, with the median slope higher than the mean slope, and the histogram peak slope less.
The high El Niño years are barely visible.
For the means, the skewness varied between -1.52 and +1.51, with a mean of 0.107.
The kurtosis varied between 1.44 and 5.74, with a mean of 2.48.
With the histogram data, it is possible to do trendology in several ways. One is of course just the monthly mean difference versus time, but the same can be with the median of the difference, and the histogram peak locations. Here are all three methods plotted, separated by 10°C for clarity. 1980 is blank because this is the baseline year. The standard deviations of the means are shown as one-sigma error bars, and are about ±1.7°C.
Notice that the slopes of the linear regression fits vary, with the median slope higher than the mean slope, and the histogram peak slope less.
The high El Niño years are barely visible.
For the means, the skewness varied between -1.79 and +1.22, with a mean of -0.226.
The kurtosis varied between 1.44 and 11.28, with a mean of 3.64.
Here are the corresponding graphs for the continental USA, there are only 146 pixels so the histograms are meaningless.
There is a lot more variance in the mean which shows up in the time-series plots:
Thanks a lot for the good, interesting job.
Glad you appreciated it.
ready to shoot up to .15 any month now
Is this small spike caused by the water vapor from the volcano that they are talking about? It seems odd that every region warmed last month.
The 7 year trend of Jan – Jul LT data shows slight cooling … https://www.youtube.com/watch?v=rVbGdJzw8KQ
1979 was a low point. So not much of a real increase. I still like to look at http://temperature.global/
Shows we are trending lower than 2015. We don’t seem to be seeing any increase in global temperature at all!
Why don’t climate scientists report the standard deviations with their 30 year averages? As a statistician I have learned that these are critical to understanding data – particularly as you are almost certainly only looking at « real » phenomena outside at least +/- 2 standard deviations. I’d like to know (and I bet lots of others would too) how many of these temperature statistics are within that +/- 2 sd range. It would also put the claimed increases measured in hundredths of a degree you read in the press into perspective.
Certainly a trend of 0.12 degrees C (+/- what error?) for a few decades (maybe an increase of .6 to 1.2 degrees per century, depending how long it lasted) seems quite consistent with a gradual return to more normal temperatures after the Little Ice Age.
There is a lot of statistical information that is dropped from the satellite data analysis, starting with the number of points averaged during a month for each grid location, and the standard deviations. For the anomaly averages, there is also a standard deviation for each grid location that are not reported.