Home/BlogThe Version 6.1 global average lower tropospheric temperature (LT) anomaly for October, 2024 was +0.73 deg. C departure from the 1991-2020 mean, down from the September, 2024 anomaly of +0.80 deg. C.
The new (Version 6.1) global area-averaged temperature trend (January 1979 through October 2024) is now +0.15 deg/ C/decade (+0.21 C/decade over land, +0.13 C/decade over oceans).
The previous (version 6.0) trends through September 2024 were +0.16 C/decade (global), +0.21 C/decade (land) and +0.14 C/decade (ocean).
The following provides background for the change leading to the new version (v6.1) of the UAH dataset.
NOTE: Snide comments which suggest someone has not read (and understood) that which follows will lead to comment privileges being revoked.
Key Points
- The older NOAA-19 satellite has now drifted too far through the diurnal cycle for our drift correction methodology to provide useful adjustments. Therefore, we have decided to truncate the NOAA-19 data processing starting in 2021. This leaves Metop-B as the only satellite in the UAH dataset since that date. This truncation is consistent with those made to previous satellites after orbital drift began to impact temperature measurements.
- This change reduces recent record global warmth only a little, bringing our calculated global temperatures more in line with the RSS and NOAA satellite datasets over the last 2-3 years.
- Despite the reduction in recent temperatures, the 1979-2024 trend is reduced by only 0.01 deg/ C/decade, from +0.16 C/decade to +0.15 C per decade. Recent warmth during 2023-2024 remains record-setting for the satellite era, with each month since October 2023 setting a record for that calendar month.
Background
Monitoring of global atmospheric deep-layer temperatures with satellite microwave radiometers (systems originally designed for daily global weather monitoring) has always required corrections and adjustments to the calibrated data to enable long-term trend detection. The most important of these corrections/adjustments are:
- Satellite calibration biases, requiring intercalibration between successively launched satellites during overlaps in operational coverage. These adjustments are typically tenths of a degree C.
- Drift of the orbits from their nominal sun-synchronous observation times, requiring empirical corrections from comparison of a drifting satellite to a non-drifting satellite (the UAH method), or from climate models (the Remote Sensing Systems [RSS] method, which I believe the NOAA dataset also uses). These corrections can reach 1 deg. C or more for the lower tropospheric (LT) temperature product, especially over land and during the summer.
- Correction for instrument body temperature effects on the calibrated temperature (an issue with only the older MSU instruments, which produced spurious warming).
- Orbital altitude decay adjustment for the multi-view angle version of the lower tropospheric (LT) product (no longer needed for the UAH dataset as of V6.0, which uses multiple channels instead of multiple angles from a single channel.)
The second of these adjustments (diurnal drift) is the subject of the change made going from from UAH v6.0 to v6.1. The following chart shows the equator crossing times (local solar time) for the various satellites making up the satellite temperature record. The drift of the satellites (except the non-drifting Aqua and MetOp satellites, which have fuel onboard to allow orbit maintenance) produces cooling for the afternoon satellites’ LT measurements as the afternoon observation transitions from early afternoon to evening. Drift of the morning satellites makes their LT temperatures warm as their evening observations transition to the late afternoon.
The red vertical lines indicate the dates after which a satellite’s data are no longer included in the v6.0 (UAH) processing, with the NOAA-19 truncation added for v6.1. Note that the NOAA-19 satellite has drifted further in local observation time than any of the previous afternoon satellites. The NOAA-19 local observation times have been running outside our training dataset which includes the assumption of a linear diurnal temperature drift with time. So we have decided it is now necessary to truncate the data from NOAA-19 starting in 2021, which we are now doing as of the October, 2024 update.
Thus begins Version 6.1 of our dataset, a name change meant to reduce confusion and indicate a significant change in our processing. As seen in the above figure, 2020 as the last year of NOAA-19 data inclusion is roughly consistent with the v6.0 cutoff times from the NOAA-18 and NOAA-14 (afternoon) satellites.
This type of change in our processing is analogous to changes we have made in previous years, after a few years of data being collected to firmly establish a problem exists. The time lag is necessary because we have previously found that two operating satellites in different orbits can diverge in their processed temperatures, only to converge again later. As will be shown below, we now have sufficient reason to truncate the NOAA-19 data record starting in 2021.
Why Do We Even Include a Satellite if it is Drifting in Local Observation Time?
The reasons why a diurnally drifting satellite is included in processing (with imperfect adjustments) are three-fold: (1) most satellites in the 1979-2024 period of record drifted, and so their inclusion was necessary to make a complete, intercalibrated satellite record of temperatures; (2) two operational satellites (usually one drifting much more than the other) provide more complete sampling during the month for our gridded dataset, which has 2.5 deg. lat/lon resolution; (3) having two (or sometimes 3) satellites allows monitoring of potential drifts, i.e., the time series of the difference between 2 satellite measurements should remain relatively stable over time.
Version 6.1 Brings the UAH Data closer to RSS and NOAA in the Last Few Years
Several people have noted that our temperature anomalies have been running warmer than those from the RSS or NOAA satellite products. It now appears this was due to the orbital drift of NOAA-19 beyond the useful range of our drift correction. The following plot (preliminary, provided to me by John Christy) shows that truncation of the NOAA-19 record now brings the UAH anomalies more in line with the RSS and NOAA products.
As can be seen, this change has lowered recent global-average temperatures considerably. For example, without truncation of NOAA-19, the October anomaly would have been +0.94 deg. C, but with only MetOp-B after 2020 it is now +0.73 deg. C.
The following table lists various regional Version 6.1 LT departures from the 30-year (1991-2020) average for the last 22 months (record highs are in red):
| YEAR | MO | GLOBE | NHEM. | SHEM. | TROPIC | USA48 | ARCTIC | AUST |
| 2023 | Jan | -0.07 | +0.06 | -0.21 | -0.42 | +0.14 | -0.11 | -0.45 |
| 2023 | Feb | +0.06 | +0.12 | +0.01 | -0.15 | +0.64 | -0.28 | +0.11 |
| 2023 | Mar | +0.17 | +0.21 | +0.14 | -0.18 | -1.35 | +0.15 | +0.57 |
| 2023 | Apr | +0.12 | +0.04 | +0.20 | -0.10 | -0.43 | +0.46 | +0.38 |
| 2023 | May | +0.29 | +0.16 | +0.42 | +0.33 | +0.38 | +0.54 | +0.13 |
| 2023 | June | +0.31 | +0.34 | +0.28 | +0.51 | -0.54 | +0.32 | +0.24 |
| 2023 | July | +0.57 | +0.60 | +0.55 | +0.83 | +0.28 | +0.81 | +1.49 |
| 2023 | Aug | +0.61 | +0.77 | +0.44 | +0.77 | +0.69 | +1.49 | +1.29 |
| 2023 | Sep | +0.80 | +0.83 | +0.77 | +0.82 | +0.28 | +1.12 | +1.15 |
| 2023 | Oct | +0.78 | +0.84 | +0.72 | +0.84 | +0.81 | +0.81 | +0.56 |
| 2023 | Nov | +0.77 | +0.87 | +0.67 | +0.87 | +0.52 | +1.07 | +0.28 |
| 2023 | Dec | +0.74 | +0.91 | +0.57 | +1.00 | +1.23 | +0.31 | +0.64 |
| 2024 | Jan | +0.79 | +1.01 | +0.57 | +1.18 | -0.19 | +0.39 | +1.10 |
| 2024 | Feb | +0.86 | +0.93 | +0.79 | +1.14 | +1.30 | +0.84 | +1.14 |
| 2024 | Mar | +0.87 | +0.95 | +0.80 | +1.24 | +0.23 | +1.05 | +1.27 |
| 2024 | Apr | +0.94 | +1.12 | +0.76 | +1.14 | +0.87 | +0.89 | +0.51 |
| 2024 | May | +0.78 | +0.78 | +0.79 | +1.20 | +0.06 | +0.23 | +0.53 |
| 2024 | June | +0.70 | +0.78 | +0.61 | +0.85 | +1.38 | +0.65 | +0.92 |
| 2024 | July | +0.74 | +0.86 | +0.62 | +0.97 | +0.42 | +0.58 | -0.13 |
| 2024 | Aug | +0.75 | +0.81 | +0.69 | +0.73 | +0.38 | +0.90 | +1.73 |
| 2024 | Sep | +0.80 | +1.03 | +0.56 | +0.80 | +1.28 | +1.49 | +0.96 |
| 2024 | Oct | +0.73 | +0.87 | +0.59 | +0.61 | +1.84 | +0.81 | +1.07 |
The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for October, 2024, and a more detailed analysis by John Christy, should be available within the next several days here. This could take a little longer this time due to the changes resulting from going from v6.0 to v6.1 of the dataset.
The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days (also possibly delayed):
Lower Troposphere:
http://vortex.nsstc.uah.edu/data/msu/v6.1/tlt/uahncdc_lt_6.1.txt
Mid-Troposphere:
http://vortex.nsstc.uah.edu/data/msu/v6.1/tmt/uahncdc_mt_6.1.txt
Tropopause:
http://vortex.nsstc.uah.edu/data/msu/v6.1/ttp/uahncdc_tp_6.1.txt
Lower Stratosphere:
http://vortex.nsstc.uah.edu/data/msu/v6.1/tls/uahncdc_ls_6.1.txt
Phew, am I glad the temperature always goes down rapidly after an el Nino.
Don’t you have something useful to do, like herding your sheep in the Swiss Alps? If you are not spreading propaganda from the Manchester Guardian you are offering inane comments.
If you don’t like what you’re getting from your methodology, just change your methodology.
*** Which is what climate modelers have been doing for over 30 years. –Roy
Is there a reason you can’t REPLY to my post instead of defecating on it?
Please point out where the land-based data has had a revision in which a 13-month average has changed by 0.12.
*** Seriously? YOU immediately defecated on my post. Regarding your challenge, go back through the last 20 years of land-based data revisions yourself and show me… I’m not doing your homework for you. Besides, please point out to ME a land-based dataset that agrees with the other land-based datasets to the level our 3 satellite datasets (UAH, RSS, NOAA) agree with each other. Meanwhile, climate models produce warming estimates that vary by a factor of THREE. I’ve grown tired of your biased rants, Stew. Bye. — Roy
I’ll take that one. Because you are trolling insufferably, not engaging in fruitful discussion, EVEN WHEN THE POINT OF THE OP WAS TO PROVIDE TOTAL TRANSPARENCY ON THE METHODOLOGY AND ITS RATIONALE.
Good on you Dr Spencer. I had to laugh when he said present proof of temp adjustments by the land based indices. In the spirit it was asserted here is a great example from Zuckerberg:
https://www.facebook.com/groups/climate.discussion/posts/2761763910658417/?notif_id=1721729175490005¬if_t=group_post_approved&ref=notif
Poor AQ.. nappy time ?
There are significant reductions to the latest monthly values. What is the trend difference to three decimal places?
barry
One classical answer is to watch
https://www.nsstc.uah.edu/data/msu/v6.0/tlt/tltglhmam_6.0.txt
as it shows 3datdp.
The other one is to await the arrival of the 2.5 degree grid data out of which it is easy to generate a time series similar to the one above, even if the grid data itself has no more to offer than 2datdp.
Barry, again
It’s already here:
https://www.nsstc.uah.edu/data/msu/v6.1/tlt/tltglhmam_6.1.txt
And the grid is ready too.
Thanks, Bindidon, it’s up far earlier than I imagined it would be.
I ran a least squares regression and to September 2024 inclusive I got:
V6.0 | 0.158 C/decade
V6.1 | 0.150 C/decade
0.08 C/decade is up there with the largest changes to trend after revision.
It looks like pretty much every anomaly back to December 1978 has been adjusted, and not symmetrically. Seems the revision includes more than truncating NOAA-19.
“ Thus begins Version 6.1 of our dataset, a name change meant to reduce confusion and indicate a significant change in our processing.”
I was wondering what you were going to try next…
https://imgur.com/a/91LoXnT
*** I’m going to give you the benefit of the doubt and assume you aren’t accusing us of dreaming up adjustments. Did you even read the post? These “afternoon” satellites drift (by design of their orbital insertion, related to the timing of data ingest needed for weather forecast model cycles), and at some point their record has to be truncated to work in our Version 6 system. This is just the latest of three such satellites requiring this over the years (decades). -Roy
“I’m going to give you the benefit of the doubt and assume you aren’t accusing us of dreaming up adjustments.”
No malice intended, it’s just that in your July 2023 update you wrote:
The October 2024 update is the first time since that you post about a review of your internal methods and procedures, and also an update of the same. Glad to see you are finding time to investigate.
“Did you even read the post?”
Yes, several times, and compared it to https://link.springer.com/article/10.1007/s13143-017-0010-y. No new revelations.
Regards.
There go my statistics.
Hopefully the data for the new version will be uploaded soon. In the mean time, using the old 6.0 data this would have been the hottest October on record, and the 16th consecutive monthly record.
Top ten warmest Octobers (Old version)
Year Anomaly
1 2024 0.94
2 2023 0.93
3 2017 0.48
4 2020 0.39
5 2021 0.38
6 2022 0.33
7 2019 0.31
8 2015 0.28
9 2016 0.28
10 1998 0.24
After this update, this is probably the second warmest October on record.
Year Anomaly
1 2024 0.73
2 2023 0.78
3 2017 0.48
4 2020 0.39
5 2021 0.38*
6 2022 0.33*
7 2019 0.31
8 2015 0.28
9 2016 0.28
10 1998 0.24
2021 and 2022 are from version 6.0.
It’s still looking certain that 2024 will be a record year by some margin. Current projections are that 2024 will be 0.75 +/- 0.06C. 2023 was 0.43C.
In contrast RSS upto September is 0.227C / decade.
(This is based on me manually updating the data from 2023, apologies if there are any errors.)
I make the old trend 0.159C / decade, and the new 0.154C / decade. The new trend is only including changes made since 2023.
“I make the old trend 0.159C / decade, and the new 0.154C / decade.”
Update – now that all the data has been published.
I make the old trend 0.159C / decade, and the new 0.151C / decade.
If UAH is now only using a single satellite, what does that do for the uncertainty estimates?
*** “Uncertainty” in what? The global anomalies and 1979-2024 trends should have greater absolute accuracy, the monthly anomalies somewhat more noise since 2021. The gridpoint anomalies will also have greater absolute accuracy (less bias) since 2021, but they will exhibit more noise. So, “uncertainty” is an imprecise term. — Roy
Roy,
It would be useful if there was a more recent uncertainty analysis published. The [Christy et al. 2003: Error Estimates of Version 5.0] publication is now over 20 years old.
*** Maybe with funding we could have worked on your suggestion. Instead, the federal government stopped supporting our dataset, likely due to the fact we *used* to get the lowest temperature trends of the 3 satellite groups (UAH, RSS, and NOAA). Now our trend is almost identical to NOAA’s (recently adjusted) trend. Go figure. One problem with uncertainty estimates is there is an unknown “structural uncertainty” component. One way is to intercompare the three satellite datasets (UAH, RSS, and NOAA) for their levels of agreement/disagreement to get some idea of uncertainty. –Roy
Yeah, that is unfortunate that your funding has dwindled. I understand that really limits what you can do. And yes, I agree the structural uncertainty is the hardest to quantify. FWIW, I did what you suggested. I performed a type A evaluation of uncertainty using RSS, UAH, and STAR and got +/- 0.15 C for the monthly anomalies. That is actually lower than the Christy et al. 2003 assessment of +/- 0.20 C. Once the v6.1 data files are published I’ll see if I can do some comparisons between v6.0 and v6.1 and between RSS and STAR and see if anything useful pops out.
I sympathize with the funding argument, but I keep arguing with people on WUWT who think that if you publish data without a full uncertainty analysis it’s tantamount to fraud. They also claim that the true uncertainties of all monthly data is in the order of 1 or 2 degrees.
I agree that comparing different data sets is a way of estimating random uncertainty, at least it puts an upper limit on it. I’ve done this comparing surface and UAH data to suggest the uncertainties are probably nothing like as large as is claimed. You can also see this just by comparing monthly figures within a single data set. It might also be interesting to compare the variability in the grid data between versions 6 and 6.1.
Roy, Maybe it is because you are associated with wackos and charlatans like Anthony Watt?
> Maybe with funding we could have worked on your suggestion
So you got funding for this new project:
https://www.drroyspencer.com/2024/11/urban-legends-of-climate-change-palm-springs-california/
Roy says: “These corrections can reach 1 deg. C or more for the lower tropospheric (LT) temperature product, especially over land and during the summer.
That sounds like a lot of uncertainty!
The 6.1 revision suggests that the RSS and NOAA methods are more reliable than the empirical method UAH is using:
1. Drift of the orbits from their nominal sun-synchronous observation times, requiring empirical corrections from comparison of a drifting satellite to a non-drifting satellite (the UAH method), or from climate models (the Remote Sensing Systems [RSS] method, which I believe the NOAA dataset also uses).
** Note the diurnal drift corrections that large are restricted to certain regions and seasons. The Earth is mostly oceans, and the corrections are small there. Regarding your claim that RSS and NOAA do better diurnal drift corrections, what do you know about the diurnal cycle in climate models? I suggest you do your research before making such pronouncements. -Roy
Roy says: “Regarding your claim that RSS and NOAA do better diurnal drift corrections, what do you know about the diurnal cycle in climate models? I suggest you do your research before making such pronouncements. -Roy”
Over the past three years, you’ve adjusted your temperature estimates with empirical corrections for drift, and now they align closely with the RSS and NOAA figures. This suggests that RSS and NOAA may have done a better job from the outset assuming, of course, that you have confidence in your revised values.
It does not suggest RSS and NOAA may have done a better job. It suggests they got lucky. Why? Because they (or at least RSS) use climate models to “correct”. Who would use climate models to correct observational data (or a statistical analysis of observational data)? The climate models aren’t observations. They are computer models. They don’t incorporate all the myriad inputs that may affect climatefor example, they can’t come anywhere close to modeling water vapor and cloudsand haven’t ever accurately forecast future temperatures. They only have a modicum of accuracy hindcasting because they have been hand-tweaked to do so. It is beyond strange that anyone would use climate models as the measuring standard instead of observational data.
They don’t use climate models “instead of” observational data.
Where did you hear this?
They don’t use ‘climate models’ as the “measuring standard” either. They use the NCAR community GCM to help estimate diurnal drift of the satellites. The observational data is the backbone of the dataset.
Curious to know where you get your information.
“the NCAR community GCM” is a model! What do you think the M stands for in General Circulation Model?
You have completely missed the point RLH. I know it’s a climate model. Read the conversation again.
Roy Spencer notes that UAH can no longer use data from NOAA-19 after 2021 because the satellite’s diurnal drift has moved outside the range where their linear diurnal drift adjustment can be applied. However, he did not explain why UAH didnt disclose this issue in 2021 and instead only recognized it retrospectively in 2024.
RSS appears to employ more complex algorithms for diurnal corrections. They combine observational data with models to predict temperature fluctuations throughout the day at different atmospheric layers (Spencer refers to these models as “climate models”). This approach may enable RSS to utilize data from individual satellites over extended periods, without the same limitations imposed by a strictly linear diurnal drift model.
If the new UAH version 6.1 accurately reflects the tropospheric temperature trend, it would suggest that RSS’s algorithms have already successfully adjusted for the non-linear drift of NOAA-19 beyond 2021. Indeed, the close correlation between UAH and RSS datasets from 2006 to 2024 bolsters confidence in the reliability of both datasets. If one groups correction methods are sound, the other’s must be as well. https://docs.google.com/presentation/d/1jb1W28JXDIPX9ndzk2uUVp8JGK4MnKsXlGpKo2HFI0Y/edit?usp=sharing
More concerning, however, is that both datasets show a warming trend of approximately 0.35 C per decade since 2006. This is far higher than the 19792024 trend reported by Spencer, indicating a significant acceleration in warming even when accounting for the recent El Nio. In this context the UAH 6.1 revision is minor, and I hope Roy Spencer will find the opportunity to address the rapid recent warming trend that his dataset shows.
The primary model for RSS diurnal correction is NCAR’s CCM3 – “Community Climate Model 3.” Roy’s terminology is correct.
Diurnal correction is trained with the GCM + raw data, and then the results compared to non-drifting satellites (AQUA, Metop A and B) to assess validity.
https://journals.ametsoc.org/view/journals/clim/29/10/jcli-d-15-0744.1.xml
Sig says: “I hope Roy Spencer will find the opportunity to address the rapid recent warming trend that his dataset shows.”
According to NASA CERES data, all the warming in the 21st century can be explained by increases in absorbed solar energy. You will never hear this from the media.
Richard M says: “According to NASA CERES data, all the warming in the 21st century can be explained by increases in absorbed solar energy. You will never hear this from the media.”
Of course, the sun is the source of almost all energy, and the critical factor for global temperature is how much is absorbed by the earth and its atmosphere. Increased absorption caused by increases in GHGs raises temperatures, which in turn decreases albedo by reducing cloud cover and melting snow and ice. I read about this in media all the time.
Sig says:
”Of course, the sun is the source of almost all energy, and the critical factor for global temperature is how much is absorbed by the earth and its atmosphere. Increased absorption caused by increases in GHGs raises temperatures, which in turn decreases albedo by reducing cloud cover and melting snow and ice. I read about this in media all the time.”
thats not what richard said!
he said: “According to NASA CERES data, all the warming in the 21st century can be explained by increases in absorbed solar energy.”
and you tried unsuccessfully to turn that in to increases in absorption of surface IR energy.
either you don’t know what you are talking about or you are doing that deliberately.
Here are the new conversion factors for the old 1981-2010 baseline period based of the v6.1 data file. For example, v6.1 anomalies as-reported on the 1991-2020 baseline are 0.142 lower than would be if using the v6.1 1981-2010 baseline.
Jan -0.142
Feb -0.161
Mar -0.128
Apr -0.122
May -0.124
Jun -0.133
Jul -0.130
Aug -0.126
Sep -0.167
Oct -0.161
Nov -0.134
Dec -0.120
Sorry did not check for any spelling mistakes with my previous comment.
Does v6.1 change the order of the warmest years since 1979? After 2024 and 2023 are 2016 and 2020 still in 3rd and 4th place?
**** I haven’t looked at this yet. –Roy
The absolute values look the same between v6.0 and v6.1.
Jan… 263.18
Feb… 263.27
Mar… 263.43
Apr… 263.84
May… 264.45
Jun… 265.10
Jul… 265.42
Aug… 265.23
Sep… 264.64
Oct… 263.95
Nov… 263.41
Dec… 263.19
Thnx bdgwx
bdgwx
You compared the climatologies for 1991-2020. They are not affected by the cutoff of NOAA-19, as the change affects only data after 2020.
But comparing the time series inevitably leads to small differences:
https://drive.google.com/file/d/17PbCMryyVwBbMUfnBZ61CCIRXaFIjCex/view
Thanks so much for ALL that you do, Roy. I don’t think you grasp just how appreciated you are – even bothering to reply to the above posts! Cheers.
From England.
I repeat my criticism of the complainers. The only way to refute this dataset is to do it differently and then demonstrate a different result. Currently, the 3 satellite datasets seem to have enough agreement for validation. More importantly, if they use different methods of any kind and still agree, then that would seem to validate the raw data as well.
Now for the trend. What is going on? How did we get a sudden surge leading to what appears to be a new normal?
So you don’t expect any change in the future that is a return to things as before 2023?
“Version 6.1 Brings the UAH Data closer to RSS and NOAA in the Last Few Years”
While the revision brings the last few years of anomalies closer to departures from other groups, it conversely draws the long-term trend further away from other groups, where UAH was (AFAIK) already the lowest trend.
That this will be an unpopular result is predictable. Do you have any comment on the resulting trend difference, Roy?
barry
” That this will be an unpopular result is predictable. ”
Certainly not among the blog’s Coolistas, let alone at WUWT.
So you dont expect any change in the future that is a return to things as before 2023?
Apparently, the transition from 6.0 to 6.1 by cutting off NOAA-19 had no visible influence on LT’s computation based on the weighting formula described by Roy Spencer in 2015:
LT: 1.538*MT 0.548*TP + 0.010*LS
At least for the Globe:
https://drive.google.com/file/d/1egD4xfLpHj6JBry0yPDYLfnwFA6F-LQK/view
I’m the last person who would come up with the stupyd idea of splitting hairs in length, but the UAH LT time series in rev 6.0 and 6.1 apparently differ not only from 2021:
https://drive.google.com/file/d/1gllWKudH8DmATvGbrmI6wTg_GaqNewWz/view
The differences prior to 2021 of course look very tiny.
Dr. Roy’s recent data, as adjusted, is in relatively good agreement with other global temperature data sets. The spike in global average temperatures that began with the advent of the last el nino is continuing, and may be turning into more of a step increase than simply a spike caused by a fairly strong el nino following a rare three-year-long la nina event.
In my most recent video I provide some insight into this spike/step in global average temperature. The link for the video is: https://youtu.be/-EVy7MkUDBg
As always, comments are welcome.
The spike began with a volcanic eruption that blew millions of tons of water into the stratosphere, a normally very arid region.
“Dr. Roy’s recent data, as adjusted, is in relatively good agreement with other global temperature data sets.”
In terms of the last few years, yes. In terms of the overall trend, this revision makes UAH more of an outlier.
barry
” In terms of the overall trend, this revision makes UAH more of an outlier. ”
I can’t agree.
Did you ever see the most recent data published by NOAA’s STAR for the LT?
https://drive.google.com/file/d/1S5Mk2vfD_kwqwlacpE8VoU-ynhq3I45Z/view
*
The time when UAH’s LT resembled STAR’s MT is long gone.
And since STAR LT is now on par with UAH LT but starts with comparatively higher temperatures, STAR’s long-term trend is inevitably lower than UAH’s.
Source
https://www.star.nesdis.noaa.gov/data/mscat/MSU_AMSU_v5.0/Monthly_Atmospheric_Layer_Mean_Temperature/Global_Mean_Anomaly_Time_Series/
Red green colourblindness doesn’t help me, sadly.
As I wrote upthread, AFAIK, UAH has the lowest trend since 1979. I wondered about STAR, that being the only dataset I haven’t checked.
barry
I’m sorry about your disease.
What colors are good for you then? I would update the graphic accordingly.
I’m not sure it’s politically correct to call it a “disease”. Computer GUI designers have to keep in mind that about one in 15 of men are red-green colour-blind, so it’s a fairly common condition. Here are some ideas:
https://visualisingdata.com/2019/08/five-ways-to-design-for-red-green-colour-blindness/
Red and greens and colours that contain em are sometimes difficult to distinguish, Bin. Especially with dots or lines. Broad primary colours are no problem, even red and green. Traffic lights are no issue, for example.
A long time ago in a forum, far, far away, an opposed interlocutor spent an hour with me establishing a colour palette I could easily distinguish, for a ‘spaghetti’ graph he posted. He reposted the graph in various hue ranges. He did it to be helpful, not to prove his point. I well remember that completely unexpected kindness.
The winning combo had more blue in it, and either no reds or no greens.
I ran the regression on NOAA STAR data, Bindidon. I see that for the available data (since 1981) it has a lower trend than UAH.
[If you’re curious about how I ‘see’ colours, click on this link and scroll down to “Normal vision vs. deuteranopia.” The two photos of surfboards look identical to me. Normal colour vision sees them as very different. However, the colours in the bottom panel look more vibrant to me than to normies. The brain compensates somehow]
If I read correctly, you now don’t use any NOAA-19 data after 2021 but do incorporate those data before that date? In this case, I assume that you think the rest of the available data are sufficient to give a fairly accurate result? If so, what do the trends look like if you remove NOAA-19 data prior to 2021, also?
Why would you do that?
Because if the sources used are sufficient now, then they should also be sufficient in earlier periods. If so, the data series can be more consistent. I don’t know if there are impediments to extending the use of only the data sources used now to earlier periods; that’s why I asked. If there are no impediments, using the reduced data sources would give us more confidence about the trend, whatever it is.
mike…the land surface record is based on thermometers spaced one per 100,000 km^2 on average. The sea surface is covered by Argo buoys with the same spacial coverage but with a major difference. Whereas the surface thermometers are contained in housings, the buoys remain immersed measuring ocean temperatures while intermittently surfacing to take a surface temperature reading. What wrong with the latter scenario?
In other words, the surface record is more fiction than fact.
This has nothing to do with what I asked.
Mike Roberts
” This has nothing to do with what I asked. ”
Exactly.
Robertson usually does not answer questions because what he has to say is much more important to him than what others write.
Robertson’s allegations about ‘the land surface record based on thermometers spaced one per 100,000 km^2 on average’ (NOAA having only 1500 stations worldwide) is a pure lie he endlessly continues to spread despite having been contradicted many times.
So what!
*
Now, what you said in your reply to ‘RLH’ (November 5, 2024 at 10:41 PM) I fully understand.
Conversely, I don’t understand Roy Spencer’s claim he made in his head post:
” This change reduces recent record global warmth only a little, bringing our calculated global temperatures more in line with the RSS AND NOAA satellite datasets over the last 2-3 years. “.
Simply because one either can agree to the LT series RSS V4.0 OR NOAA STAR V5.0 but not to both.
When you compare, for Jan 2020 till now,
– UAH 6.0
https://drive.google.com/file/d/1SzeQ5ErgvyEhq0NYWUkcOyPsja_IcQLt/view
to
– UAH 6.1
https://drive.google.com/file/d/1JODsoc0CBVhDI3zj9aTIgjuhXRUm3Zmd/view
you clearly see that while UAH 6.0 drifts away from NOAA STAR but not from RSS, UAH 6.1 conversely drifts away from RSS but keeps on par with NOAA STAR, what probably was intended because UAH LT is much nearer to STAR than to RSS.
Roy…I don’t envy you your task of trying to put together a global satellite record in this day of uncertainty as to what is going on with the warming. Thanks for the explanation and the difficulties you face dealing with aging sats and their aging orbits.
In the 40+ years of the sat record, nothing like what we are seeing today has occurred. Then again, no humans have had a chance to observe weather conditions over a significant period of time with the instruments and coverage available today.
Just to stay in character, here’s an opinion piece from the Grauniad: “A rebuke to those who said clean power by 2030 was unachievable: they were wrong, we were right”
https://www.theguardian.com/commentisfree/2024/nov/05/clean-power-2030-labour-neso-report-ed-miliband
That rebuke has already been shown to be nonsense by Homewood, amongst other analysts. The pursuit of Net Zero continues to be a total disaster for the UK population. Just like the Grauniad itself.
> amongst other analysts
Paul isn’t an analyst.
What else would one expect from the Guardian, a fake news outlet that supplants the National Enquirer of the US.
Dr, Roy – Thank you for the explication of your corrections for satellite drift. It’s clear that one cannot just hang a thermocouple from the satellite and just let it plough through the atmosphere, and it’s useful to be reminded of the mess and effort involved in genuine scientific work.
This is one of the most ignorant and patronizing posts aimed at Roy I have yet encountered. Not surprising from an uber-alarmist who lacks the scientific understanding to reply in kind and relies on a fake news rag as his authority figure.
Not only that, he hides behind software that blocks replies he deems to be unreliable. Erog, he lacks the spine to receive criticism.
barry…Out of curiosity, how do you visualize white light, which is a good proportion of red and green frequencies? In other words, it is receptors in the eye that average R, G, and B to produce white light. If you don’t see R and G well then white should appear bluish.
I don’t notice any blue tinge to white.
I have deuteranopia. Click this and scroll down a tiny bit to see, roughly, what I see. The 2 pics are identical to me.
https://www.healthline.com/health/eye-health/what-do-colorblind-people-see#visual-differences-in-images
Differences since Jan 2020 for UAH LT Globe: rev 6.0 vs. rev 6.1
compared to RSS 4.0 LT and NOAA STAR 5.0 LT
6.0
https://drive.google.com/file/d/1SzeQ5ErgvyEhq0NYWUkcOyPsja_IcQLt/view
6.1
https://drive.google.com/file/d/1JODsoc0CBVhDI3zj9aTIgjuhXRUm3Zmd/view
Appreciate the colours. I can tell the difference between the purple and the blue here, which is sometimes tricky due to the red in purple. I don’t see red well, so I sometimes mistake purple for blue.
Similarly, I could use orange instead of red anywhere: you then see the remaining yellow :–)
But plotting data in yellow is bad for us because it’s not so terribly visible.
Would you see brown as a mix of red and green? Or is it as gray as the two plotted separately?
Yeah, brown can be tough.
I would have no problem distinguishing blue, black, bright orange and dark green.
Thanks for being interested, Bin.
To answer your question, depending on the type of brown, it can be hard to distinguish between green or red.
Between these tricky colours, very dark vs very light is better. So a very dark brown and a light green would probably be ok.
Thicker lines would also help, but then your graphs would be less elegant. I’m not into less elegant graphs.
Well, it looks like we’ve surrendered to climate change overnight.
Humans have always had to adapt to climate change throughout history, and that will never stop. The next ice age could be just around the corner.
The cycle of major ice ages, caused by the Milankovitch cycle, started when CO2 dropped below 300ppm. It is now 420ppm. Does this mean no more major ice ages?
Almost certainly not, but the next one may be delayed.
Eventually we’ll run out of fossil fuels to burn and the ‘excess’ CO2 will be naturally sequestered over millennia.
It seems like a natural thing to look at the difference between the versions, that is, subtract V6.0 from V6.1 monthly.
https://southstcafe.neocities.org/climate/UAH_difference_6p1_-_6p0.png
Several observations about this.
1) There is a jitter between the two datasets from the beginning to 2013. This is presumably due to finite arithmetic and it’s not large, but suggests something in the processing got rounded or truncated before it needed to be.
2) The difference in the datasets seems to start circa 2013, well before the cutoff of NOAA-19 data. Presumably this is because the diurnal drift adjustment applied is different when using the shorter useful lifetime.
3) There is strong divergence after NOAA-19 is dropped after 2021, as would be expected.
4) Not illustrated in the linked plot, there is divergence between UAH and RSS circa 2005 which some have attributed to differences in handling the diurnal drift of NOAA-15 and RSS’s inclusion of NOAA-16 data. I’m curious how confident Dr Spencer is in the diurnal drift correction for NOAA-15 given that the acknowledged limitations for NOAA-19.
Mark B
Thank you very much for your excellent observation.
Mark, a query on the jitter: the difference for every month prior to 2013 is either 0.003 or 0.004. But your differencing graph has many values on the zero line (no difference) prior to 2013?
The difference prior to 2009 is either 0.00 or 0.01. To 2021 we see difference values of 0.00, -0.01, and -0.02. After 2021 we see much larger negative differences, that is v6.0 runs hotter than the new v6.1 by quite a bit.
The text files (links below) have resolution of 0.01 degree C, so the difference in the datasets prior to 2013 is in the least significant digit.
https://www.nsstc.uah.edu/data/msu/v6.1/tlt/uahncdc_lt_6.1.txt
https://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Obviously the text file is rounded or truncated when saved to the file and presumably has more resolution in the computation than the text file. One would expect that if all the computations prior to the introduction of NOAA-19 data circa 2009 were identical between 6.1 and 6.0, the difference would be precisely 0. They aren’t, which is curious and would bother me if I were generating the dataset, but it isn’t obviously a significant problem.
In the period where NOAA-19 is used, 2009 to 2021 in v6.1, the difference goes negative, and, arguably trends more negative so the contribution of NOAA-19 over that period is different. I’ve hypothesized this is from the diurnal drift adjustment over the shorter period of inclusion, but that’s idle speculation. In any case it results in a difference that grows to something on the order of 0.01 C/decade during the period NOAA-19 is used in both data sets.
The jump at the end of the record circa 2022 when METOP-B is used exclusively is about 0.1 C in a couple years which is very large in this context.
I would be interested in a more in depth explanation of the change, but it does suggest that the methodology is rather sensitive to somewhat subjective decisions in the generation of the dataset.
Mark,
For the very first value, December 1978 LT data, I get
v6 : -0.482
v6.1 : -0.479
Difference of 0.003. I subtracted every monthly value in Excel and got differences of 0.003 and 0.004 up to 2013.
https://www.nsstc.uah.edu/data/msu/v6.0/tlt/tltglhmam_6.0.txt
https://www.nsstc.uah.edu/data/msu/v6.1/tlt/tltglhmam_6.1.txt
How are you getting differences of 0.00? Are we looking at different data?
Doh, we are looking at different data. Mine is to 3 decimal places.
Mark,
Roy advises:
“a more detailed analysis by John Christy, should be available within the next several days here.”
http://nsstc.uah.edu/climate/
I clicked on the link and…
This page isn’t working
nsstc.uah.edu didn’t send any data.
ERR_EMPTY_RESPONSE
Perhaps the link will activate when J Christy posts.
barry…FYI…I got onto the site using the Tor browser. The time and date will be listed with my post.
It’s the same site I have posted many times that show the UAH temperature contours.
Barry,
Indeed, we were using different source files, mine had two decimal places and the one you linked has three decimal places.
Here’s the picture with the higher resolution data:
https://southstcafe.neocities.org/climate/UAH_difference_6p1_-_6p0_rev1.png
This also has one least significant digit “jitter” which is now 0.001 C up to 2013, then what appears to be two discrete steps down, about 0.01 C at 2013 and 0.005 C at 2017 before the down slope at 2021. The steps are in the lower resolution file as well, but are obscured coarser quantification.
Given the discrete steps, this looks more like a bug in the code than what I had hypothesized up thread.
It will be interesting to see what Dr Christy has to say.
Your theory that it’s rounding may yet be correct if the data processed has greater than 3 decimal points, which I think is likely.
Mark B, barry
Just for fun: the same comparison now based on absolute values obtained by recombination of grid anomalies and climatology.
1. Absolute data
https://drive.google.com/file/d/1lfxd274RLncir0hSXNe2iSHLg5XteB9a/view
2. Differences (6.0 minus 6.1)
https://drive.google.com/file/d/1JGKYhqvumAaD71Yvb_WbEAPdzmB4v_lR/view
Till end of 2020, the differences don’t bypass 0.003; they are probably due to the fact that the grid data also has a precision of 2 digits atdp.
There is a good chance that Climate Change will be put on hold for at least the next 4 years. The stock market is betting that oil stocks have a strong future. I would think companies that do fracking are a good investment.
For those who are not happy with the new President, I blame Crazy Nancy, Chuck, and the rest of the Democrats who tried to elect a very liberal and poorly qualified candidate. There are several Governors who would have been competitive, or they could have been honest the public and had the hard discussion with Joe a year ago. The primary process is a good way to select a candidate for the general election.
I’m not a US guy of course but doubt your idea would have had success.
Trump mania reached such paroxysmal proportions this year that no Democratic candidate would have had a chance.
How can you be sure the Democratic Party isn’t simply incompetent?
They ran a good campaign under the circumstances, had massive turnout at rallies (ie, good organising), and apart from a few gaffes ran a fairly tight ship.
The people around Trump also ran a strong campaign, and managed, it seems, to have kept some reign on a very chaotic candidate.
It had nothing to do with a mania, Trump-like or otherwise. clearly, most voters stuck to party lines since the final popular vote was only 4% in Trump’s favour.
The US all-or-nothing system produced Trump as winner. If one person wins a state, he/she gets all of the electoral votes allotted to that state. Trump simply won key states that the Dems won last time.
I think the US electorate was simply fed up with Dem activity that turned them off. Such as…
-allowing a flood of illegal immigrants into the country
-pushing sexual fantasies like gender identity for children when the children have not reached the age of sexual feeling. That include sexual fantasies like men claiming to be women and being allowed onto women’s sport teams and using female restrooms.
-escalating the climate change propaganda to the point they were passing legislation preventing people heating their homes with natural gas and going so far as to ban certain types of refrigerators or gas stoves.
All of this based on an unproved theory of climate change.
-generally, forcing a politically-correct lifestyle on people.
-defunding police and prosecuting police for doing their jobs arresting known criminals.
-giving criminals a get-out-of-jail free card by lowering or eliminating bail requirements.
The above affected a small percentage of US voters influencing them to vote against that system. I don’t blame them.
barry…a few gaffes???
The major gaffe was anointing Kamala Harris, a black Barbie Doll, for President. At no time did she even try to offer substances and spent far too much time going after Trump, going so far as to compare him to fascists like Hitler. She recruited the likes of Oprah Winfrey, hardly a representative of intelligence.
The Dems thought Harris would appeal to the Black voters, especially women. The rocket-scientist, Obama, even scolded Black men for not supporting her. Pretty racist.
In New York, Harris campaigned for the Jewish vote then in another state campaigned for the Muslim vote, trying to woo them by calling for an amnesty in Gaza.
The women completely lacks substance, or even an image of it. All she did was smile a lot and appear on populist programs like Saturday Night Live, an over the hill, yuppie program that officially died some 20 years ago.
People argue that Trump lacks substance and maybe so. However, he presented himself in a way that appeals to many voters, like serving at a McDonald’s restaurant and arriving at a site in a garbage truck, to counter the Biden claim that his supporter’s are garbage.
He also recruited Elon Musk and Robert Kennedy Jr., two heavy-weight and popular figures. He has granted Kennedy the task to Make America Healthy Again. Kennedy has already declared his intention to clean out corruption in the FDA and CDC, welcome news to my ears.
Junior is up to a great start:
https://www.cnn.com/2024/11/03/health/rfk-jr-fluoride-science/index.html
“poorly qualified candidate”
Harris has held official positions in law and politics for 20+ years, prosecuted justice and helped create bills. Attorney General of California for 6 years (an elected position), was in the Senate for 4 years after that, and then became vice-president for another 4.
If you think she is “poorly qualified,” how did you assess Trump on that in 2016?
The presidential election season lasts about 15 months or more. The primaries run from February through June, but there is a lot of “testing the waters” and fund raising before then. Somewhat like the military industrial complex, the political news media complex is good for the news business. There is a lot of advertising spending that most people hate to watch anyway — go figure.
Kamala Harris ran in 2020 and got 2% of the primary vote before she dropped out. Joe knew about his Biden Crime Family problems when he ran, so my theory is that he picked Kamala as impeachment insurance. This is why the media coverup of Hunter’s laptop is such an important issue with Trump supporters. It probably helped his campaign this time. In the same way, the legal issues probably helped him seem like a victim of the “swamp”. That one lady did make a lot of money with the department store allegation.
I didn’t notice “Hunters laptop” in the top 5 issues in exit polls.
What is clear is ‘its the economy stupid ‘ was at the top.
barry…”I have deuteranopia”.
***
Read the article and apparently your eyes lack the cone that detects green light frequencies. I would research that if I were you for the following reasons…
1)I don’t know if there are optical instruments that can differentiate one cone from the other. Ergo, you may have the cones and your brain is interpreting the signals from them.
2)Read a book recently on the brain and the author is an expert on the visual centres of the brain. The system he described is seriously complex with certain brain neurons specifically receiving colour signals from certain cones while other neurons receive data re the brightness levels. The entire system of neurons is in layers and very complex.
I am wondering if there may be some way to get your brain to output the proper colours for green. Experts will howl with laughter at my naivete but I am speaking from experience not theory. The brain can change to accommodate different conditions. I am sure someone has researched this already.
For example, if a person has been blind from birth and is suddenly granted sight, apparently that person becomes confused by the images he/she encounters. It takes some time for the brain to learn how to process the images. In fact, that’s how a child learns, by the brain adjusting to the environment.
I believe it’s too deep-wired to be cured. It is a genetic condition that is realized in the eye. There are “colour-correcting” glasses which can make it easier to distinguish colours, but do not provide colour fidelity.
barry…”I dont notice any blue tinge to white”.
***
This is of interest since there is no colour called white as we know it. In other words, receptors in the eye (cones) receive basically red light, green light and blue light but to see white light, the receptors must add R, G and B in a certain proportion. If green is missing in the receptors, you should not be able to see white. That is further complicated by the fact there are not an equal number of R, G, B receptors.
If you consider a light source like the Sun, it appears to be almost white, although no one should look directly at it to verify that, even for a few seconds. The colour we see is filtered by the atmosphere but the wavelengths emitted range from about 380 nm (violet) to about 600 nm (red). In frequency that is 668 Thz (violet) to 400 Thz (red).
The Sun emits no colours, it is receptors in our eyes that convert EM frequencies/wavelengths to colour. In your case, it appears that the receptors are not picking up a range of frequencies from about 526 Thz to 606 Thz.
But how can that be if you can see white. The white you see is a precise combination of red, green and blue. Without green you should see white as a combination of red and blue, so your white should be a magenta colour, a pinkish/purple.
At the link you provided, there are white emblems on the surf boards 4th from left, 8th and 9th from left. They are more light gray than white. Can you see them OK? The confusing part is that these whites appear equally on both photos.
If you are seeing white like the people with normal visual acuity it appears your green receptors are working fine.
It could be that your rods are compensating. The rods in the eye apparently detect black and white and are used for night vision. That’s likely why it’s easier to see colour in brighter light. However, it would also appear that white should appear duller and not nearly as bright as can be received by the cones.
THE NON-LINEARITY OF THE S-B EMISSION LAW is a kind of approach to the planet surface emission behavior, when considering two identical planets absorbing the same amount of incident EM energy as HEAT. And then, the planets IR emiting the same exactly amount of outgoing energy.
So the faster rotating planet’s surface would have the less differentiated temperature, and the higher average surface temperature.
Thus, when a planet rotates faster, all other things the same, it is considered that the planet absorbs the same amount of HEAT, no matter how much faster the planet rotates.
But when a planet rotates faster, all other things the same, the planet actually absorbs a larger amount of HEAT.
And, of course, that larger amount of absorbed HEAT is IR emitted too, so the radiative energy balance
( Energy in = Energy out ) to be necessarily met.
The faster rotation leads to a larger amount of absorbed HEAT, that is what makes it very POWERFUL the Solar Irradiated planet surface Rotational Warming Phenomenon (N*cp )^1/16 true.
The ROTATIONAL WARMING PHENOMENON amplifies the planet average surface temperature.
So, the planetary surface (N*cp ) product is one of the planet average surface temperature deterministic parameters.
–
https://www.cristos-vournas.com
I’ve got to admit, the Petroleum Engineer in me does not hate the sound of this…
By ‘we’, I hope he is not including the vast Tar Sands that belong to Canada.
I have no problem with intelligent environmentalists who understand that we humans have an equal right to exist as other life forms. The eco-loonies see it differently, they think human life should be extinguished and the planet left to other forms of life. Based on that dogma, they think it’s OK to cut off our supply of life giving fossil fuels by a certain date, such as 2030 or 2050.
Until we find a good alternative to fossil fuels we humans NEED them. That is not negotiable, we need those fossil fuels to survive. The eco-loonies don’t care about humans and their needs, they want fossil fuel usage reduced to zero, no matter the consequences.
To enable their perverted demands, they have invented a pseudo-science to support their mania. They have perverted science and they are using that perverted science to brainwash the naive into believing it.
The eco-loonies have taken a simple concept…greenhouse warming…and perverted it to fit the atmosphere. Anyone who has experienced the warmth in a real greenhouse understand that heating due to solar energy but I doubt that most people have an intuitive understanding of why the real greenhouse warms. They don’t know you can cool the greenhouse by opening doors and windows, making it obvious to the scientific mind that real greenhouse warming is about blocking natural convection and has nothing to do with the 0.04% of CO2 in greenhouse air.
Eco-loonies have latched onto an experiment by Tyndall circa 1850 in which he discovered that CO2 absorbs infrared energy, in this case from the Sun. Tyndall had no idea why the same CO2 warmed when it absorbed IR, that discovery would not come till 1913 when Bohr hypothesized the relationship between electrons orbiting atom and electromagnetic energy.
The eco-loonies have totally ignored Bohr’s finding and attached themselves mentally to Tyndall’s simple finding that CO2 absorbs IR and warms. Neither TYndall nor later, Arrhenius, tried to apply the Ideal Gas Law to CO2 to see exactly how much heat it could produce in the atmosphere by absorbing surface IR and warming. Neither tried to apply the heat diffusion equation to see how much heat produced by CO2 could be transferred to the entire atmosphere.
Rather, both offered unscientific assessments of how much CO2 could warm the atmosphere. Even at that, both Tyndall and Arrhenius agreed that CO2 warming would be beneficial, not catastrophic, as modern eco-loonies have claimed, based only on unvalidated climate models. Those models were programmed by people who obviously misunderstood the findings of Tyndall and the demands of the Ideal Gas Law and the heat diffusion equation.
Both the IGL and the heat diffusion equation are based on the limitations of heat transfer due to the mass percent of gases in a mixed gas. In the case of CO2, its mass percent of roughly 0.06% limits the amount of heat it can transfer to 0.06C per 1C warming of the entire atmosphere. The eco-loonies have raised that percent to a range between 9% and 25%, based on some kind of pseusdo-science.
Yes, exactly, the CO2 is a trace gas in Earth’s thin atmosphere. CO2 is so infinitesimal content makes plants starving.
Yes, the CO2 is a trace gas in Earths thin atmosphere. CO2 is so infinitesimal content makes plants starving.
The natural processes slowly remove CO2 from atmosphere. There were less and less CO2 for plants – it was heading to an eventual ecological catastrophe.
Volcanism emissions weakened, so plants were only recycling the already available atmospheric CO2.
But yet, some of it continuosly was sequestered in coal, nat. gas and oil sediments. And in various minerals too.
The industrialization based on fossil fuels burning came just in time. It came just in time to save life on planet from an otherwise inevitable ecological catastrophe.
–
https://www.cristos-vournas.com
Gotta love the ‘skeptic’ contradictions.
Increased CO2 will be good for crops!
CO2 is a ‘trace gas’ and adding more has virtually no effect!
I don’t think we’ve had a proper Energy Czar since the 1970’s… https://www.reuters.com/world/us/trump-considering-doug-burgum-new-energy-tsar-slash-regulations-ft-reports-2024-11-08/
I would have preferred Scott Sheffield, but Burgum’s fine.
SOLAR MINIMUM UPDATE
The French ski resort Alpe du Grand Serre has closed permanently just ahead of the 2024-25 winter ski season due to the impacts of our warming world. The popular skiing and snowboarding destination couldn’t afford to transition to a year-round destination to cope with its shrinking snow season.
https://www.thecooldown.com/outdoors/alpe-du-grand-serre-ski-resort-france-closed-permanently-climate-change/
Drill, baby. Drill!
That can only be good for the economy.
Was it a good idea for the ski resort to close permanently?:
https://climate.rutgers.edu/snowcover/chart_seasonal.php?ui_set=eurasia&ui_season=4
https://climate.rutgers.edu/snowcover/chart_seasonal.php?ui_set=eurasia&ui_season=1
How are these graphs relevant?
Is there a point behind this handwaving?
Why show that one has not read?
https://www.climatecentral.org/climate-matters/snowfall-trends-2024
Alpe du Grand Serre is in Eurasia.
Skiing depends on snow cover.
Why show that one is trying to deflect?
Alpine skiing usually happens on mountains.
Categories promoted by Alexander Dugin should stay in Nazbol’s books.
Global for Oct 2024
https://climatedatablog.wordpress.com/wp-content/uploads/2024/11/uah-global.jpeg
Tropics for Oct 2024
https://climatedatablog.wordpress.com/wp-content/uploads/2024/11/uah-tropics.jpeg
I’ve made a few maps showing the difference between 6.1 and 6.0 at a local level.
One thing thing of note is the differing effects between land and ocean. Here’s the difference between the annual average for 2021, and much of the land area has been warmed by the change, including all of Africa and Australia. And there’s a very large warming correction over the Himalayas.
https://i.imgur.com/KSv84ry.png